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 communications filed on 05/11/2023.
Claims 2-18 and 38-55 have been canceled.
Claims 1 and 19-37 are pending and have been examined.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted was filed on 05/11/2023. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
The use of a trade name or a mark used in commerce (e.g. WIMAX, etc.) has been noted in this application. It should be capitalized (each letter) wherever it appears and be accompanied by the generic terminology or, where appropriate, include a proper symbol indicating use in commerce, such as ™, SM, or ® following the word. Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Appropriate correction is required.
Claim Objections
Claims 1 and 19-20 are objected to because of the following informalities:
As per claim 1, it appears that “the basis of” in line 9 should be replaced with e.g. “based on” (note: there is lack of antecedent basis for “the basis”). This similarly applies to claims 19-20.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “management node… configured to… select… compare…”, etc. in claims 20-37.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 20-37 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claims purport to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required (i.e. uses single means) by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim.
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 and 19-37 are 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 “improve”/”improved” in claim 1 is a relative term which renders the claim indefinite. The term “improve”/”improved” 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. What is considered “improve”/”improved” varies depending on person, context, etc. As such, the claim is indefinite. Independent claims 19 and 20 also recite the same limitations and therefore have the same problem. Due at least to their dependency upon claim 20, dependent claims 21-37 (note: claims 23, 26, and 29 also include the term) also are indefinite.
Further as per claim 1, there is lack of antecedent basis for “the agent” in the beginning of line 5. There is lack of antecedent basis for “the one or more types of parameters” in line 10 (note: only “one or more parameters” is previously recited). There is lack of antecedent basis for “the data distribution for the first agent” in line 13. There is lack of antecedent basis for “the data distribution for the second agent” in line 13. It is unclear whether “the data distributions” in line 14 is referring to “the data distribution for the first agent” and “the data distribution for the second agent”, whether it also includes the “data distribution for the agent”, or is different. Independent claims 19 and 20 also recite the same limitations and therefore have the same problem. Due at least to their dependency upon claim 20, dependent claims 21-37 also are 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 and 19-37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method, product, and node comprising selecting, comparing, initiating, and determining.
The limitation “selecting… comparing… initiating… and determining…” as recited in claim 1 are each a process, under the broadest reasonable interpretation, covering performance of the limitations in the mind or by pen and paper (See Berkheimer v. HP, Inc., 881 F.3d 1360, 1366, 125 USPQ2d 1649 (Fed. Cir. 2018)) but for the recitation of generic computer components. That is, other than reciting “computer-implemented”, the limitation “selecting a set of similar agents from the plurality of agents that improve a first parameter of the system, wherein similar agents are selected on the basis of similarities or matches in the one or more types of input data, the one or more types of parameters of the system improved by the agent, and the one or more types of output data” in the context of the claim encompasses the user making observations and determinations. The limitation “for a first agent and a second agent in the selected set of similar agents, comparing (302) the data distribution for the first agent to the data distribution for the second agent to determine a relationship between the data distributions” in the context of the claim encompasses the user making evaluations. The limitation “initiating generation of one or more candidate agents based on the determined relationship” in the context of the claim encompasses the user making evaluations. The limitation “determining whether to replace one or both of the first agent and the second agent with the one or more candidate agents based on a comparison of a performance of the one or more candidate agents with respect to a performance of the first agent and/or a performance of the second agent” in the context of the claim encompasses the user making a judgement. If a claimed limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements. The claim recites “computer-implemented”. The elements are 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 (e.g. See MPEP 2106.05(f)). Note that an “agent”/“agents”, at best, amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)), but “agent”/“agents” need not be interpreted in this manner and broadly can be any entity. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/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 elements are no more than a generic computer component. Therefore, the claims are not patent eligible.
Claims 19 and 20 also recite similar claim language as claim 1, and thus have the same issues. It is noted, with respect to claim 19, that the claim recites “non-transitory computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a suitable computer or processor, the computer or processor is caused to perform” to perform the method. The elements are 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 (e.g. See MPEP 2106.05(f)). It is noted, with respect to claim 20, that the claim further recites “a management node” to perform the method. The elements are recited at a high-level of generality, such that it, at best, amounts to no more than mere instructions to apply the exception using a generic computer component (e.g. See MPEP 2106.05(f)). Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea and are not sufficient to amount to significantly more than the judicial exception.
Regarding claim 21, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely describes further determining which is a mental step (encompassing a user making evaluations), and does not include any additional elements.
Regarding claim 22, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely describes further determining and updating, which are mental steps (encompassing a user making evaluations), and does not include any additional elements.
Regarding claim 23, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely describes providing input data and receiving output data, which is considered as insignificant extra-solution activity and MPEP 2106.05(d)(II) indicates that mere sending and receiving data is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here; note e.g. Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362, OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014), etc.).
Regarding claim 24, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely describes repeating the mental steps and does not include any additional elements.
Regarding claim 25, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely describes repeating the mental steps and does not include any additional elements.
Regarding claim 26, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes evaluating, which is a mental step (encompassing a user making evaluations), and does not include any additional elements.
Regarding claim 27, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes comparing, which is a mental step (encompassing a user making evaluations), and does not include any additional elements.
Regarding claim 28, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes determining and applying, which are mental steps (encompassing a user making evaluations), and does not include any additional elements.
Regarding claim 29, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes selecting, which is a mental step (encompassing a user making a determination), and does not include any additional elements.
Regarding claim 30, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further includes generator and discriminator neural networks, which amounts to generally linking the use of the judicial exception to a particular technological environment or field of use (e.g. see MPEP 2106.05(h)).
Regarding claim 31, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further includes using auto-encoders, which amounts to generally linking the use of the judicial exception to a particular technological environment or field of use (e.g. see MPEP 2106.05(h)).
Regarding claim 32, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely further describes the determined relationship, which is part of the mental steps and does not include any additional elements.
Regarding claim 33, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely describes further determining, which are mental steps (encompassing a user making a determinations), and does not include any additional elements.
Regarding claim 34, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely describes defining and further determining, which are mental steps (encompassing a user making a determinations), and does not include any additional elements.
Regarding claim 35, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely describes defining and further determining, which are mental steps (encompassing a user making a determinations), and does not include any additional elements.
Regarding claim 36, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim merely describes further determining, which is a mental step (encompassing a user making a determinations), and does not include any additional elements.
Regarding claim 37, the claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception. For example, the claim further includes a telecommunication network, which amounts to generally linking the use of the judicial exception to a particular technological environment or field of use (e.g. see MPEP 2106.05(h)).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1, 19-25, 27-29, and 32 are rejected under 35 U.S.C. 103 as being unpatentable over Iashyn et al. (US 20200272859 A1) in view of Anderson et al. (US 20210225508 A1).
As per independent claim 1, Iashyn teaches a computer-implemented method performed by a management node for use with a cognitive layer, CL, that is used to improve or manage a system, wherein the CL comprises a plurality of agents (e.g. in paragraphs 47, 99 and 101, “environment 300 shown in FIG. 3 includes a machine learning structure controller 302 and first network edge device 302-1 N-network edge device 302-N (“network edge devices 304… machine learning structure controller 302 can manage machine learning model training and instantiation at network edge devices” and figure 3 showing layer 304 of edge devices, i.e. cognitive layer of agents), wherein the agents have respective agent information, the respective agent information indicating one or more types of input data required by a model implemented by the agent, one or more parameters of the system that are to be improved by the agent, one or more types of output data provided by the model implemented by the agent (e.g. in paragraphs 56, 62-63, 71, 77, 98, and 101-102, “performance reports… a type of machine learning model… applicable parameters/hyperparameters defining a machine learning model… types of activation functions… output created from the machine learning model as a result of training… characteristics of the network edge devices… data of a specific type of IoT device will be used to train [i.e. input]… amount of computational resources used to train and execute a model, a speed at which a model is trained and execute, adaptability of a model to variable training data, and other applicable parameters… performs better or worse [i.e. to be improved]”), wherein the method comprises:
(i) selecting a set of agents from the plurality of agents that improve a first parameter of the system, wherein agents are selected on the basis of the one or more types of parameters of the system improved by the agent and the one or more types of output data (e.g. in paragraphs 56, 71, 77, 98, and 101-102, “performance reports indicating performance of the machine learning model at the network edge devices can be received by the machine learning structure controller… determine whether to deploy another machine learning model architecture to the network edge devices based on the performance reports of the machine learning model received from the network edge devices… output created from the machine learning model as a result of training… amount of computational resources used to train and execute a model, a speed at which a model is trained and execute, adaptability of a model to variable training data, and other applicable parameters… performs better or worse” and figure 3);
(ii) for a first agent and a second agent in the selected set of agents, comparing information for the first agent to information for the second agent (e.g. in paragraphs 56, 98, and 101-102, “determine whether to deploy another machine learning model architecture to the network edge devices based on the performance reports of the machine learning model received from the network edge devices… the experimental machine learning model 508 performs better or worse than the first machine learning model 506” and figure 3);
(iii) initiating generation of one or more candidate agents (e.g. in paragraphs 101-102, “if the experimental machine learning model 508 is performing worse than the first machine learning model 506…the machine learning structure controller 302 can deploy the modified experimental machine learning model architecture 504 or new experimental machine learning model”); and
(iv) determining whether to replace one or both of the first agent and the second agent with the one or more candidate agents based on a comparison of a performance of the one or more candidate agents with respect to a performance of the first agent and/or a performance of the second agent (e.g. in paragraphs 20, 55, 69, 98, and 101-102, “performs better or worse… if the experimental machine learning model 508 outperforms the first machine learning model 506 then the machine learning structure controller 302 can instruct the network edge devices 304 to discard the first machine learning model 506 and replace it with the experimental machine learning model… if the experimental machine learning model 508 is performing worse than the first machine learning model 506…the machine learning structure controller 302 can deploy the modified experimental machine learning model architecture 504 or new experimental machine learning model”),
but does not specifically teach a data distribution for the agent, selecting similar agents, wherein agents are selected on the basis of similarities or matches in the one or more types of input data and wherein comparing includes comparing the data distribution for the first agent to the data distribution for the second agent to determine a relationship between the data distributions and generation of one or more candidate agents based on the determined relationship.
However, Anderson teaches a data distribution for an agent(s) (e.g. in paragraph 13, “data distributions 16A, 16B, 16C, 16D, 16E, 16F of some of the trained models”), selecting a set of similar agents, wherein similar agents are selected on the basis of similarities or matches in one or more types of input data (e.g. in paragraphs 12 and 95, “using a plurality of models that were trained on a data distribution or distributions that were most similar to that of the new data set”), for a first agent and a second agent in the selected set of similar agents, comparing a data distribution for the first agent to a data distribution for the second agent to determine a relationship between the data distributions (e.g. in paragraph 81, “comparison of the P(match) distribution…for each of the candidate models”), and generating one or more candidate agents based on the determined relationship (e.g. in paragraph 72, 74-75, 78, and 80, “form a set of candidate models… determine a probability of match between the candidate model 84 and the target data set 80. The candidate model 84 may be described by vector m, which is representative of the weights of the candidate model… data vector d which comprises data x of the data sample and a corresponding set of data activations a… select a number (for example, 2, 3, 4 or 5) of candidate models 84 having the highest… P(match) value”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Iashyn to include the teachings of Anderson because one of ordinary skill in the art would have recognized the benefit of facilitating greater performance.
Claim 19 is the product claim corresponding to method claim 1, and is rejected under the same reasons set forth and the combination further teaches a non-transitory computer readable medium having computer readable code embodied therein, the computer readable code being configured such that, on execution by a computer or processor, the computer or processor is caused to perform operations (e.g. Iashyn, in paragraph 21, “a one or more processors and at least one computer-readable storage medium storing instructions which, when executed by the one or more processors, cause the one or more processors to”).
Claim 20 is the product claim corresponding to method claim 1, and is rejected under the same reasons set forth and the combination further teaches a management node (e.g. Iashyn, in paragraph 21, “a machine learning structure controller”).
As per claim 21, the rejection of claim 20 is incorporated and the combination further teaches wherein operation (iv) comprises determining that one or both of the first agent and the second agent should be replaced if the performance of the one or more candidate agents exceeds the performance of the first agent and/or the second agent (e.g. Iashyn, in paragraphs 101-102, “if the experimental machine learning model 508 outperforms the first machine learning model 506 then the machine learning structure controller 302 can instruct the network edge devices 304 to discard the first machine learning model 506 and replace it with the experimental machine learning model… machine learning structure controller 302 can use a machine learning structure search to define a new experimental machine learning model architecture if the experimental machine learning model 508 is performing worse than the first machine learning model 506. Subsequently, the machine learning structure controller 302 can deploy the modified experimental machine learning model architecture 504 or new experimental machine learning model”).
As per claim 22, the rejection of claim 20 is incorporated and the combination further teaches (v) if it is determined to replace one or both of the first agent and the second agent with the one or more candidate agents, update the plurality of agents by replacing the one or both of the first agent and the second agent in the plurality of agents with the one or more candidate agents in the plurality of agents (e.g. Iashyn, in paragraphs 101-102, “machine learning structure controller 302 can instruct the network edge devices 304 to discard the first machine learning model 506 and replace it with the experimental machine learning model… modify the experimental machine learning model architecture 504 if the experimental machine learning model 508 is performing worse”).
As per claim 23, the rejection of claim 22 is incorporated and the combination further teaches (vi) when the first parameter of the system is to be improved, provide input data to the agents in the updated plurality of agents that are to improve the first parameter and receiving output data from the agents in the updated plurality of agents that are to improve the first parameter (e.g. Iashyn, in paragraphs 56 and 72, “performance reports of the machine learning model received from the network edge devices… learning models, e.g. in being trained using telemetry data” to “improve” “performance of the machine learning models”).
As per claim 24, the rejection of claim 20 is incorporated and the combination further teaches repeating operations (ii)-(iv) for other pairs of agents in the selected set of similar agents (e.g. Iashyn, in paragraphs 56, 98, and 101-102, “determine whether to deploy another machine learning model architecture to the network edge devices based on the performance reports of the machine learning model received from the network edge devices… the experimental machine learning model 508 performs better or worse than the first machine learning model 506… replace it… deploy the modified experimental machine learning model architecture 504 or new”, and figure 3 showing other pairs of agents).
As per claim 25, the rejection of claim 20 is incorporated and the combination further teaches wherein operations (ii)-(iv) are repeated for all possible pairs of agents in the selected set of similar agents (e.g. Iashyn, in paragraphs 56, 98, and 101-102, “determine whether to deploy another machine learning model architecture to the network edge devices based on the performance reports of the machine learning model received from the network edge devices… the experimental machine learning model 508 performs better or worse than the first machine learning model 506… replace it… deploy the modified experimental machine learning model architecture 504 or new”, and figure 3 showing all pairs of agents).
As per claim 27, the rejection of claim 20 is incorporated and the combination further teaches (iii-a) compare the performance of the one or more candidate agents with respect to the performance of the first agent and/or the performance of the second agent (e.g. Iashyn, in paragraphs 101-102, “if the experimental machine learning model 508 outperforms the first machine learning model 506 then the machine learning structure controller 302 can instruct the network edge devices 304 to discard the first machine learning model 506 and replace it with the experimental machine learning model… modify the experimental machine learning model architecture 504 if the experimental machine learning model 508 is performing worse”).
As per claim 28, the rejection of claim 27 is incorporated and the combination further teaches wherein operation (iii-a) comprises: determining the performance of the one or more candidate agents by applying the data distribution for the first agent to the one or more candidate agents, and applying the data distribution for the second agent to the one or more candidate agents (e.g. Iashyn, in paragraphs 101-102, “if the experimental machine learning model 508 outperforms the first machine learning model 506 then the machine learning structure controller 302 can instruct the network edge devices 304 to discard the first machine learning model 506 and replace it with the experimental machine learning model… modify the experimental machine learning model architecture 504 if the experimental machine learning model 508 is performing worse”; Anderson in paragraph 12, “using a plurality of models that were trained on a data distribution or distributions that were most similar to that of the new data set”).
As per claim 29, the rejection of claim 20 is incorporated and the combination further teaches wherein operation (i) comprises selecting two agents for the set of similar agents for which there is at least a partial match between the respective input data required by the two agents, at least a partial match between the respective output data provided by the two agents, and at least a partial match between the parameters of the system improved by the two agents (e.g. Iashyn, in paragraphs 62-63, 71, and 77, “a type of machine learning model.. applicable parameters/hyperparameters defining a machine learning model… types of activation functions… output created from the machine learning model as a result of training… characteristics of the network edge devices… data of a specific type of IoT device… adaptability of a model to variable training data” and figure 3 showing models that are part of the same edge device, which would have at least partial matches in respective information).
As per claim 32, the rejection of claim 20 is incorporated and the combination further teaches wherein the determined relationship between the data distributions indicates whether (i) the data distribution for the first agent is identical or substantially identical to the data distribution for the second agent, (ii) the data distribution for the first agent is contained within the data distribution for the second agent, (iii) the data distribution for the first agent contains the data distribution for the second agent, or (iv) the data distribution for the first agent is disjoint from the data distribution for the second agent (e.g. Anderson, in paragraphs 80-81, “compares the P(match) distribution…for each candidate model… based on the comparison of the P(match) distribution… P(match) value that is above a threshold value”; note that (i)-(iv) cover all possible distributions and the use of “or”).
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over Iashyn et al. (US 20200272859 A1) in view of Anderson et al. (US 20210225508 A1) and further in view of DeFelice et al. (US 20190236139 A1).
As per claim 26, the rejection of claim 20 is incorporated and the combination further teaches wherein operation (i) comprises evaluating similarities or matches using the required one or more types of input data, the one or more types of parameters of the system improved by the agent and provided one or more types of output data for the agents (e.g. Iashyn, in paragraphs 56, 62-63, 71, and 77, “performance reports… a type of machine learning model… applicable parameters/hyperparameters defining a machine learning model… types of activation functions… output created from the machine learning model as a result of training… characteristics of the network edge devices… data of a specific type of IoT device will be used to train… amount of computational resources used to train and execute a model, a speed at which a model is trained and execute, adaptability of a model to variable training data, and other applicable parameters… performs better or worse”; Anderson, in paragraphs 60 and 81, “model representations may be processed such that like-for-like nodes or filters are matched… comparison of the P(match) distribution and/or aggregated P(match) value for each of the candidate models”), but does not specifically teach using semantic analysis of information. However, DeFelice teaches using semantic analysis of information (e.g. in paragraph 66, “identification is performed using a latent semantic analysis (LSA) technique”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of DeFelice because one of ordinary skill in the art would have recognized the benefit of facilitating information identification.
Claim 30 is rejected under 35 U.S.C. 103 as being unpatentable over Iashyn et al. (US 20200272859 A1) in view of Anderson et al. (US 20210225508 A1) and further in view of Archuleta et al. (US 20220067445 A1).
As per claim 30, the rejection of claim 20 is incorporated and the combination further teaches wherein operation (ii) comprises comparing models that represent the data distribution for the first agent to models that represent the data distribution for the second agent to determine the relationship between the data distributions (e.g. Anderson, in paragraph 81, “comparison of the P(match) distribution and/or aggregated P(match) value for each of the candidate models”), but does not specifically teach wherein models include generator and discriminator neural networks. However, Archuleta teaches items including generator and discriminator neural networks (e.g. in paragraphs 17-18 and 150, “a signal data signature distribution that the discriminator of a generative adversarial network has been trained to detect… goal of the generator is to fool the discriminator”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Archuleta because one of ordinary skill in the art would have recognized the benefit of facilitating use of well-known models (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Iashyn et al. (US 20200272859 A1) in view of Anderson et al. (US 20210225508 A1) and Archuleta et al. (US 20220067445 A1) and further in view of Ryan et al. (US 20200387797 A1).
As per claim 31, the rejection of claim 30 is incorporated and the combination further teaches wherein operation (ii) comprises using models to compare the data distributions (e.g. Anderson, in paragraph 81, “comparison of the P(match) distribution and/or aggregated P(match) value for each of the candidate models”), but does not specifically teach wherein models include auto-encoders. However, Ryan teaches models including auto-encoders (e.g. in paragraphs 9 and 15, “One example would be using PCA, or an autoencoder… learns data distribution”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Ryan because one of ordinary skill in the art would have recognized the benefit of facilitating use of well-known models (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
Claims 33-36 are rejected under 35 U.S.C. 103 as being unpatentable over Iashyn et al. (US 20200272859 A1) in view of Anderson et al. (US 20210225508 A1) and further in view of Xu et al. (US 20180150036 A1).
As per claim 33, the rejection of claim 32 is incorporated, but the combination does not specifically teach wherein if the data distribution for the first agent is identical or substantially identical to the data distribution for the second agent, operation (iii) comprises:(a) determining a candidate agent as a combination of the first agent and the second agent using federated averaging learning; or (b) determining a first candidate agent by retraining the first agent using the data distribution for the first agent and the data distribution for the second agent, and determining a second candidate agent by retraining the second agent using the data distribution for the first agent and the data distribution for the second agent. However, the combination teaches the data distribution for the first agent and the data distribution for the second agent associated with training data (e.g. Anderson, in paragraphs 12 and 95, “using a plurality of models that were trained on a data distribution or distributions that were most similar to that of the new data set”) and Xu teaches wherein if a data distribution for a first agent is identical or substantially identical to a data distribution for a second agent, operation comprising: determining a first candidate agent by retraining the first agent using previous training data and determining a second candidate agent by retraining the second agent using previous training data (e.g. in paragraph 18, “a data distribution P(Xt)… models may be retrained or incrementally updated with valid training data from recent experience”, note: this would include all scenarios of data distribution including if the data distribution for the first agent is identical or substantially identical to the data distribution for the second agent; note also “or”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Xu because one of ordinary skill in the art would have recognized the benefit of maintaining optimal performance.
As per claim 34, the rejection of claim 32 is incorporated, but the combination does not specifically teach wherein if the data distribution for the first agent contains the data distribution for the second agent, operation (iii) comprises: (c) defining the first agent as a candidate agent to replace the second agent; (d) determining a candidate agent by retraining the first agent using the data distribution from the first agent and the data distribution from the second agent; or (e) determining a candidate agent by performing transfer learning for the first agent using the data distribution from the second agent. However, the combination teaches the data distribution for the first agent and the data distribution for the second agent associated with training data (e.g. Anderson, in paragraphs 12 and 95, “using a plurality of models that were trained on a data distribution or distributions that were most similar to that of the new data set”) and Xu teaches wherein if the data distribution for a first agent contains a data distribution for a second agent, operation comprising: determining a candidate agent by retraining the first agent using previous training data (e.g. in paragraph 18, “a data distribution P(Xt)… models may be retrained or incrementally updated with valid training data from recent experience”, note: this would include all scenarios of data distribution including if the data distribution for the first agent contains the data distribution for the second agent; note also “or”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Xu because one of ordinary skill in the art would have recognized the benefit of maintaining optimal performance.
As per claim 35, the rejection of claim 32 is incorporated, but the combination does not specifically teach wherein if the data distribution for the second agent contains the data distribution for the first agent, operation (iii) comprises:(f) defining the second agent as a candidate agent to replace the first agent; (g) determining a candidate agent by retraining the second agent using the data distribution from the first agent and the data distribution from the second agent; or (h) determining a candidate agent by performing transfer learning for the second agent using the data distribution from the first agent. However, the combination teaches the data distribution for the first agent and the data distribution for the second agent associated with training data (e.g. Anderson, in paragraphs 12 and 95, “using a plurality of models that were trained on a data distribution or distributions that were most similar to that of the new data set”) and Xu teaches wherein if the data distribution for the second agent contains the data distribution for the first agent, operation comprising: determining a candidate agent by retraining the second agent using previous training data (e.g. in paragraph 18, “a data distribution P(Xt)… models may be retrained or incrementally updated with valid training data from recent experience”, note: this would include all scenarios of data distribution including if the data distribution for the second agent contains the data distribution for the first agent; note also “or”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Xu because one of ordinary skill in the art would have recognized the benefit of maintaining optimal performance.
As per claim 36, the rejection of claim 32 is incorporated, but the combination does not specifically teach wherein if the data distribution for the first agent and the data distribution for the second agent are disjoint, operation (iii) comprises:(j) determining a candidate agent by training a candidate agent using the data distribution from the first agent and the data distribution from the second agent. However, the combination teaches the data distribution for the first agent and the data distribution for the second agent associated with training data (e.g. Anderson, in paragraphs 12 and 95, “using a plurality of models that were trained on a data distribution or distributions that were most similar to that of the new data set”) and Xu teaches wherein if a data distribution for a first agent and a data distribution for a second agent are disjoint, operation comprises: determining a candidate agent by training a candidate agent using previous training data (e.g. in paragraph 18, “a data distribution P(Xt)… models may be retrained or incrementally updated with valid training data from recent experience”, note: this would include all scenarios of data distribution including if the data distribution for the first agent and the data distribution for the second agent are disjoint). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Xu because one of ordinary skill in the art would have recognized the benefit of maintaining optimal performance.
Claim 37 is rejected under 35 U.S.C. 103 as being unpatentable over Iashyn et al. (US 20200272859 A1) in view of Anderson et al. (US 20210225508 A1) and further in view of Ryan et al. (US 20200387797 A1).
As per claim 37, the rejection of claim 20 is incorporated, but the combination does not specifically teach wherein the system is a telecommunication network and the one or more parameters are operational parameters of the telecommunication network. However, Ryan teaches a system being a telecommunication network and one or more parameters being operational parameters of the telecommunication network (e.g. in paragraphs 61 and 85, “Machine Learning (ML) for networking applications, telecommunications, as well as many other applications… use relevant Performance Monitoring (PM) data along with other data to describe the behavior of a telecommunications network”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of the combination to include the teachings of Ryan because one of ordinary skill in the art would have recognized the benefit of operating over well-known networks (also amounts a simple substitution that yields predictable results [e.g. see KSR Int'l Co v. Teleflex Inc., 550 US 398,82 USPQ2d 1385,1396 (U.S. 2007) and MPEP 2143(B)]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For example,
Rausch et al. (US 10552739 B1) teaches “replacement of some or all of the existing suggestion models 2740 in the set of suggestion models 2740 that are stored by the coordinating device 2500 may be deemed desirable in situations where it may be determined that a different type of model (e.g., a different type of decision tree) has been determined to be a better choice” (US 10552739 B1).
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/W.W/Examiner, Art Unit 2144 01/10/2026
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144