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 .
Status of Claims
The present application is being examined under the claims filed 03/27/2024.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/27/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Regarding Claim 8
Claim 8 and its dependents are objected to because of the following informalities: “corresponding related to” should read “∷” should read “comprising:[[:]]”. Appropriate correction is required.
Regarding Claims 13 and 20
Claims 13 and 20 are objected to because of the following informalities: “predefined threshold” should read “specified threshold” to reflect the claims from which they depend. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 for containing an abstract idea without significantly more.
Regarding Claim 1:
Step 1 – Is the claim to a process, machine, manufacture, or composition of matter?
Yes, the claim is to a process.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites the abstract ideas of:
identifying at least one second ML application registered with the network training platform based on the first one or more parameters — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to an observation of a system to find a machine learning application.
identifying second one or more parameters related to the at least one second ML application — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to an observation of a system to find machine learning parameters.
comparing the first one or more parameters with the second one or more parameters related to the at least one second ML application — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating data to determine their similarities and differences.
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application. The additional elements:
A method for sharing data between machine learning (ML) applications by a network training platform, the method comprising: receiving a request to register a first ML application with the network training platform, wherein the request comprises first one or more parameters related to the first ML application — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
and sharing, with the first ML application, predicted data corresponding to the at least one second ML application based on the comparing —This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, the claim does not recite additional elements which amount to significantly more than the abstract idea itself. The additional elements as identified in step 2A prong 2:
A method for sharing data between machine learning (ML) applications by a network training platform, the method comprising: receiving a request to register a first ML application with the network training platform, wherein the request comprises first one or more parameters related to the first ML application — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
and sharing, with the first ML application, predicted data corresponding to the at least one second ML application based on the comparing —This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Regarding Claim 2
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
wherein the first one or more parameters comprises at least one of cell identification, slice identification, site identification, key performance indicator (KPI) to train, timestamp of data to use, an accuracy of predicted data, and an identification of a ML model associated with the first ML application — This limitation is directed to merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) as it merely limits the field of the first one or more parameters.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
wherein the first one or more parameters comprises at least one of cell identification, slice identification, site identification, key performance indicator (KPI) to train, timestamp of data to use, an accuracy of predicted data, and an identification of a ML model associated with the first ML application — Merely limiting a judicial exception to a particular field of use (see MPEP 2106.05(h)) cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 3
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
comprising: identifying a level of similarity between the first ML application and the at least one second ML application based on the comparison of the first one or more parameters with the second one or more parameters; comparing the identified level of similarity with a specified threshold — This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating data to determine their similarities and differences.
Step 2A Prong 2:
and sharing, with the first ML application, the predicted data corresponding to the at least one second ML application based on the level of similarity between the first ML application and the at least one second ML application being greater than the specified threshold — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
and sharing, with the first ML application, the predicted data corresponding to the at least one second ML application based on the level of similarity between the first ML application and the at least one second ML application being greater than the specified threshold — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 4
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
prior to sharing the predicted data corresponding to the at least one second ML application with the first ML application, modifying the predicted data based on the first one or more parameters — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
prior to sharing the predicted data corresponding to the at least one second ML application with the first ML application, modifying the predicted data based on the first one or more parameters — This limitation is recited at a high level of generality and amounts to mere data gathering of storing and retrieving information in memory, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 5
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 2:
further comprising: transmitting, to the at least one second ML application, a request to modify the predicted data corresponding to the at least one second ML application based on the first one or more parameters — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
further comprising: transmitting, to the at least one second ML application, a request to modify the predicted data corresponding to the at least one second ML application based on the first one or more parameters — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 6
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 3 which included an abstract idea (see rejection for claim 3). The claim recites the additional limitations:
Step 2A Prong 2:
comprising: training the first ML application, to obtain predicted data corresponding to the first one or more parameters of the first ML application based on the level of similarity between the first ML application and the at least one second ML application being less than the specified threshold — This limitation is directed to mere instructions to apply a judicial exception. Using ordinary machine learning training to apply a judicial exception (see MPEP 2106.05(f)) is insufficient to integrate the judicial exception into a practical application. Even if the training is implemented on a generic computer (see MPEP 2106.05(f)(2), 2106.04(d)), the limitation does not integrate the judicial exception into a practical application.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
comprising: training the first ML application, to obtain predicted data corresponding to the first one or more parameters of the first ML application based on the level of similarity between the first ML application and the at least one second ML application being less than the specified threshold — Mere instructions to apply a judicial exception (see MPEP 2106.05(f)) and using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 7
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1). The claim recites the additional limitations:
Step 2A Prong 1:
wherein sharing the predicted data corresponding to the at least one the second ML application based on the comparing comprises: validating a policy corresponding to sharing of the predicted data of the at least one second ML application —This limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using pen and paper (see MPEP 2106.04(a)(2) III. C.). The limitation is directed to a mental process because it amounts to evaluating data based on a rule to check if the rule is satisfied or not.
Step 2A Prong 2:
and sharing the predicted data of the at least one of the plurality of second ML applications based on successful validation of the policy — This limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
The additional elements as identified in step 2A prong 2:
and sharing the predicted data of the at least one of the plurality of second ML applications based on successful validation of the policy — This limitation is recited at a high level of generality and amounts to mere data gathering of transmitting and receiving data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.), which cannot amount to significantly more than the judicial exception.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 8
Independent claim 8 is a computer apparatus claim corresponding to method claim 1, which was directed to an abstract idea, therefore the same rejection and rationale applies. The only difference is that claim 8 recites the following additional elements treated under step 2A prong 2 and step 2B:
Step 2A Prong 2:
An apparatus for a network training platform for sharing data between machine learning (ML) applications, comprising: a memory storing instructions; and at least one processor configured to, when executing the instructions, cause the apparatus to perform operations comprising:: — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
An apparatus for a network training platform for sharing data between machine learning (ML) applications, comprising: a memory storing instructions; and at least one processor configured to, when executing the instructions, cause the apparatus to perform operations comprising:: — Using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 9
Dependent claim 9 is a computer apparatus claim corresponding to method claim 2, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 10
Dependent claim 10 is a computer apparatus claim corresponding to method claim 3, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 11
Dependent claim 11 is a computer apparatus claim corresponding to method claim 4, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 12
Dependent claim 12 is a computer apparatus claim corresponding to method claim 5, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 13
Dependent claim 13 is a computer apparatus claim corresponding to method claim 6, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 14
Dependent claim 14 is a computer apparatus claim corresponding to method claim 7, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 15
Independent claim 15 is a non-transitory computer-readable medium claim corresponding to method claim 1, which was directed to an abstract idea, therefore the same rejection and rationale applies. The only difference is that claim 15 recites the following additional elements treated under step 2A prong 2 and step 2B:
Step 2A Prong 2:
A non-transitory computer readable storage medium storing instructions which, when executed by at least one processor of an apparatus for a network training platform for sharing data between machine learning (ML) applications, cause the apparatus to perform operations, the operations comprising: — This limitation is directed to merely applying an abstract idea using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.04(d)).
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d) I.), failing step 2A prong 2.
Step 2B:
A non-transitory computer readable storage medium storing instructions which, when executed by at least one processor of an apparatus for a network training platform for sharing data between machine learning (ML) applications, cause the apparatus to perform operations, the operations comprising: — Using a generic computer as a tool (see MPEP 2106.05(f)(2), 2106.05(d)) cannot amount to significantly more than the judicial exception itself.
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding Claim 16
Dependent claim 16 is a non-transitory computer-readable medium claim corresponding to method claim 2, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 17
Dependent claim 17 is a non-transitory computer-readable medium claim corresponding to method claim 3, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 18
Dependent claim 18 is a non-transitory computer-readable medium claim corresponding to method claim 4, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 19
Dependent claim 19 is a non-transitory computer-readable medium claim corresponding to method claim 5, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Regarding Claim 20
Dependent claim 20 is a non-transitory computer-readable medium claim corresponding to method claim 6, which was directed to an abstract idea, therefore the same rejection and rationale applies.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 7-11, and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over NPL reference Tsay et al. “AIMMX: Artificial Intelligence Model Metadata Extractor” in view of Vo et al. (PGPUB no. US20230101955A1) herein referred to as Vo.
Regarding Claim 1
Tsay teaches:
A method for sharing data between machine learning (ML) applications by a network training
(page 82 column 2 paragraph 1) “As an example of a tool that is able to leverage extracted metadata, we implemented a catalog web application for the discovery and evaluation of AI models.”
wherein the request comprises first one or more parameters related to the first ML application;
(page 88 column 2 last paragraph) “While the model list itself is browsable by users, the main method of discovering models is through the search and filter features, which allow for querying or selecting multiple attributes that are based on properties in extracted model metadata. The model list view contains a side panel with metadata attributes for filtering such as domain, frameworks, and tags.”
identifying at least one second ML application registered with the network training platform based on the first one or more parameters;
(page 88 column 2 last paragraph) “While the model list itself is browsable by users, the main method of discovering models is through the search and filter features, which allow for querying or selecting multiple attributes that are based on properties in extracted model metadata.”; [*Examiner notes: The query itself is a request with parameters related to the first ML application while the models found as a result of the search query are the at least one second ML application]
identifying second one or more parameters related to the at least one second ML application;
(page 88 column 2 section 4.2) “Models in this catalog are added through providing GitHub repository URLs which are then passed to AIMMX for metadata extraction. The metadata are then inserted into the catalog's document database, validated automatically, and then made available for discovery. The system is available as an online service”; (page 89 column 1 paragraph 2) “Once a user selects a model, the individual model detail view enables users to assess a model for reuse. Details shown include information such as tags, extraction source, authors, and license.”
Tsay does not explicitly teach:
receiving a request to register a first ML application with the network training platform
comparing the first one or more parameters with the second one or more parameters related to the at least one second ML application;
and sharing, with the first ML application, predicted data corresponding to the at least one second ML application based on the comparing.
However, Vo teaches:
receiving a request to register a first ML application with the network training platform
(paragraph [0029]) “In step 204, the processing system may define a proposal for a proposed (e.g., first) machine learning model. In one example, the proposal defines a deployment need of the proposed machine learning model. For instance, the proposed machine learning model may be needed to identify candidates for a job opening, to filter email, to navigate an unmanned vehicle, or the like.”
comparing the first one or more parameters with the second one or more parameters related to the at least one second ML application;
(paragraph 33) “In one example, the similarity between the proposed machine learning model and the existing machine learning model may be assigned a score, and the existing machine learning model may be identified as similar to the proposed machine learning model when the score at least meets a predefined threshold score”; (paragraph 14) “. In one example, similarity may be evaluated based on similarities in parameters including model performance, model inputs, and model outputs.”
and sharing, with the first ML application, predicted data corresponding to the at least one second ML application based on the comparing.
(paragraph [0047]) “In step 210, the processing system may build a new machine learning model (e.g., a third machine learning model) that is consistent with the proposal for the proposed machine learning model by reusing a portion of the existing machine learning model.”; (paragraph [0047]) “Several techniques for machine learning from other usages can be utilized within step 210. In one example, a simple serial concatenation of two models (e.g., the existing machine learning model and a smaller adaptation model) may be utilized together. In another example, a mixture of experts model may be trained to combine one or more existing machine learning models, with the output of each existing machine learning model numerically weighted for a combined or fused mixture of models.” ; [*Examiner notes: The broadest reasonable interpretation of sharing predicted data includes any sharing or re-using of a machine learning model in order to make predictions. When a machine learning model is shared and used to make predictions, the predictions are shared in the process.]
Tsay, Vo, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the machine learning sharing as taught by Tsay by implementing the sharing based on comparisons as taught by Vo because (Vo paragraph [0013]) “For instance, machine learning models are often reused during the creation (e.g., building and training) phase. However, the deployment or runtime phase may also benefit from model reuse. For example, computing environments that are similar to previous computing environments, data sources that are similar to previously used data sources, and microservices that are similar to previously used microservices can be reused when deploying a new machine learning model. Downstream reusable application programming interface (API) connections may enable new machine learning models being deployed.”
Regarding Claim 2
Tsay in view of Vo teaches:
The method of claim 1
(see rejection of claim 1)
And Vo further teaches:
wherein the first one or more parameters comprises at least one of cell identification, slice identification, site identification, key performance indicator (KPI) to train, timestamp of data to use, an accuracy of predicted data, and an identification of a ML model associated with the first ML application.
(paragraph [0014]) “In one example, similarity may be evaluated based on similarities in parameters including model performance, model inputs, and model outputs”
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Tsay with Vo for the same reasons given in claim 1 above.
Regarding Claim 3
Tsay in view of Vo teaches:
The method of claim 1
(see rejection of claim 1)
And Vo further teaches:
comprising: identifying a level of similarity between the first ML application and the at least one second ML application based on the comparison of the first one or more parameters with the second one or more parameters; comparing the identified level of similarity with a specified threshold;
(paragraph 33) “In one example, the similarity between the proposed machine learning model and the existing machine learning model may be assigned a score, and the existing machine learning model may be identified as similar to the proposed machine learning model when the score at least meets a predefined threshold score. For instance, each parameter by which similarity may be measured (e.g., input, target, users, datasets, etc.) may be assigned an individual score, and the individual scores may be combined to form a final score.”
and sharing, with the first ML application, the predicted data corresponding to the at least one second ML application based on the level of similarity between the first ML application and the at least one second ML application being greater than the specified threshold.
(paragraph [0047]) “Several techniques for machine learning from other usages can be utilized within step 210. In one example, a simple serial concatenation of two models (e.g., the existing machine learning model and a smaller adaptation model) may be utilized together. In another example, a mixture of experts model may be trained to combine one or more existing machine learning models, with the output of each existing machine learning model numerically weighted for a combined or fused mixture of models.”
PNG
media_image1.png
420
606
media_image1.png
Greyscale
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Tsay with Vo for the same reasons given in claim 1 above.
Regarding Claim 4
Tsay in view of Vo teaches:
The method of claim 1
(see rejection of claim 1)
Vo further teaches:
further comprising: prior to sharing the predicted data corresponding to the at least one second ML application with the first ML application, modifying the predicted data based on the first one or more parameters.
(paragraph [0047]) “Several techniques for machine learning from other usages can be utilized within step 210. In one example, a simple serial concatenation of two models (e.g., the existing machine learning model and a smaller adaptation model[*Examiner notes: modifying the predicted data]) may be utilized together.”; (paragraph [0048]) “In one example, metadata associated with the new machine learning model may indicate both the existing machine learning model(s) utilized to build the new machine learning model as well as any modifications that may have been made to the existing machine learning model(s) to adapt the existing machine learning model(s) to the use case for the proposed machine learning model[*Examiner notes: based on first one or more parameters].”
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Tsay with Vo for the same reasons given in claim 1 above.
Regarding Claim 7
Tsay in view of Vo teaches:
The method of claim 1
(see rejection of claim 1)
Vo further teaches:
wherein sharing the predicted data corresponding to the at least one the second ML application based on the comparing comprises: validating a policy corresponding to sharing of the predicted data of the at least one second ML application;
(paragraph [0023]) “Any existing machine learning models (or components thereof) which are identified as candidates for reuse may be validated against the proposal and ranked according to fitness for reuse, as discussed in greater detail below.”
and sharing the predicted data of the at least one of the plurality of second ML applications based on successful validation of the policy
(paragraph [0048]) “It is assumed in step 210 that the existing machine learning model has been validated for fitness as described above. A specification for the new machine learning model may indicate the existing machine learning model that was reused, as well as the contributions of any other new or existing machine learning model components (e.g., versions, sources, etc.).”; Figure 2
PNG
media_image2.png
475
686
media_image2.png
Greyscale
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to combine Tsay with Vo for the same reasons given in claim 1 above.
Regarding Claim 8
Claim 8 is a computer apparatus claim corresponding to method claim 1. The only difference is that claim 8 recites an apparatus with a memory and processor:
Vo teaches:
An apparatus for a network training platform for sharing data between machine learning (ML) applications, comprising: a memory storing instructions; and at least one processor configured to, when executing the instructions, cause the apparatus to perform operations comprising::
(paragraph [0064]) “The processor executing the computer readable or software instructions relating to the above described method can be perceived as a programmed processor or a specialized processor. As such, the present module 405 for building and deploying a machine learning model (including associated data structures) of the present disclosure can be stored on a tangible or physical (broadly non-transitory) computer-readable storage device or medium, e.g., volatile memory, non-volatile memory, ROM memory, RAM memory, magnetic or optical drive, device or diskette, and the like.”
It would have been further obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the machine learning sharing as taught by Tsay in view of Vo by using the computer as taught by Vo because (Vo paragraph [0064]) “The processor executing the computer readable or software instructions relating to the above described method can be perceived as a programmed processor or a specialized processor. As such, the present module 405 for building and deploying a machine learning model (including associated data structures) of the present disclosure can be stored on a tangible or physical (broadly non-transitory) computer-readable storage device or medium, e.g., volatile memory, non-volatile memory, ROM memory, RAM memory, magnetic or optical drive, device or diskette, and the like.”
The remaining limitations of the claim are taught by the rejection of claim 1.
Regarding Claim 9
Claim 9 is a computer apparatus claim corresponding to method claim 2. The only difference is that claim 9 recites a computer and memory as taught in the rejection of claim 8 above. The remaining limitations of the claim are taught by the rejection of claim 2.
Regarding Claim 10
Claim 10 is a computer apparatus claim corresponding to method claim 3. The only difference is that claim 10 recites a computer and memory as taught in the rejection of claim 8 above. The remaining limitations of the claim are taught by the rejection of claim 3.
Regarding Claim 11
Claim 11 is a computer apparatus claim corresponding to method claim 4. The only difference is that claim 11 recites a computer and memory as taught in the rejection of claim 8 above. The remaining limitations of the claim are taught by the rejection of claim 4.
Regarding Claim 14
Claim 14 is a computer apparatus claim corresponding to method claim 7. The only difference is that claim 14 recites a computer and memory as taught in the rejection of claim 8 above. The remaining limitations of the claim are taught by the rejection of claim 7.
Regarding Claim 15
Claim 15 is a non-transitory computer-readable medium claim corresponding to method claim 1. The only difference is that claim 15 recites a non-transitory computer-readable medium:
Vo teaches:
A non-transitory computer readable storage medium storing instructions which, when executed by at least one processor of an apparatus for a network training platform for sharing data between machine learning (ML) applications, cause the apparatus to perform operations, the operations comprising:
(paragraph [0064]) “The processor executing the computer readable or software instructions relating to the above described method can be perceived as a programmed processor or a specialized processor. As such, the present module 405 for building and deploying a machine learning model (including associated data structures) of the present disclosure can be stored on a tangible or physical (broadly non-transitory) computer-readable storage device or medium, e.g., volatile memory, non-volatile memory, ROM memory, RAM memory, magnetic or optical drive, device or diskette, and the like.”
It would have been further obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the machine learning sharing as taught by Tsay in view of Vo by using the non-transitory computer-readable medium as taught by Vo because (Vo paragraph [0064]) “The processor executing the computer readable or software instructions relating to the above described method can be perceived as a programmed processor or a specialized processor. As such, the present module 405 for building and deploying a machine learning model (including associated data structures) of the present disclosure can be stored on a tangible or physical (broadly non-transitory) computer-readable storage device or medium, e.g., volatile memory, non-volatile memory, ROM memory, RAM memory, magnetic or optical drive, device or diskette, and the like.”
The remaining limitations of the claim are taught by the rejection of claim 1.
Regarding Claim 16
Claim 16 is a non-transitory computer-readable medium claim corresponding to method claim 2. The only difference is that claim 16 recites a computer-readable medium as taught in the rejection of claim 15 above. The remaining limitations of the claim are taught by the rejection of claim 2.
Regarding Claim 17
Claim 17 is a non-transitory computer-readable medium claim corresponding to method claim 3. The only difference is that claim 17 recites a computer-readable medium as taught in the rejection of claim 15 above. The remaining limitations of the claim are taught by the rejection of claim 3.
Regarding Claim 18
Claim 18 is a non-transitory computer-readable medium claim corresponding to method claim 4. The only difference is that claim 18 recites a computer-readable medium as taught in the rejection of claim 15 above. The remaining limitations of the claim are taught by the rejection of claim 4.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Tsay in view of Vo, and further in view of Yu et al. (PGPUB no. US20220302513A1) herein referred to as Yu.
Regarding Claim 5
Tsay in view of Vo teaches:
The method of claim 1
(see rejection of claim 1)
Tsay in view of Vu does not explicitly teach:
further comprising: transmitting, to the at least one second ML application, a request to modify the predicted data corresponding to the at least one second ML application based on the first one or more parameters.
However, Yu teaches:
further comprising: transmitting, to the at least one second ML application, a request to modify the predicted data corresponding to the at least one second ML application based on the first one or more parameters.
(paragraph [0023]) “The vehicle battery management system measures a battery parameter of the vehicle by using the sensor, and sends first battery parameter data obtained through measurement to the cloud battery management system; the cloud battery management system trains second battery parameter data, and sends a first training result obtained through training to the vehicle battery management system, where the second battery parameter data includes the first battery parameter data and historical battery parameter data; and the vehicle battery management system further updates the decision processing module based on the first training result.”
Tsay, Vo, Yu, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the machine learning sharing as taught by Tsay in view of Vo by using transmission requests to modify the predicted data as taught by Yu because (Yu paragraph [0009]) “The vehicle BMS can perform learning and training based on massive data, and can perform model fine-tuning with reference to unique data of the vehicle BMS. When the decision processing module is updated on this basis, precision, real-time performance, and reliability of the model are improved. In brief, the vehicle BMS has a capability of performing model training and decision processing based on massive cloud data and the unique data of the vehicle BMS.” That is, requesting machine-learning model fine-tuning (as a way to modify the predicted data to closer align with the desired prediction) improves precision, performance, and reliability of models.
Regarding Claim 12
Claim 12 is a computer apparatus claim corresponding to method claim 5. The only difference is that claim 12 recites a computer and memory as taught in the rejection of claim 8 above. The remaining limitations of the claim are taught by the rejection of claim 5.
Regarding Claim 19
Claim 19 is a non-transitory computer-readable medium claim corresponding to method claim 5. The only difference is that claim 12 recites a computer-readable medium as taught in the rejection of claim 15 above. The remaining limitations of the claim are taught by the rejection of claim 5.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tsay in view of Vo, and further in view of NPL reference Kallianpur et al. (PGPUB no. US20220230024A1) herein referred to as Kallianpur.
Regarding Claim 6
Tsay in view of Vo teaches:
The method of claim 3
(see rejection of claim 3)
Tsay in view of Vo does not explicitly teach:
comprising: training the first ML application, to obtain predicted data corresponding to the first one or more parameters of the first ML application based on the level of similarity between the first ML application and the at least one second ML application being less than the specified threshold
However, Kallianpur teaches:
comprising: training the first ML application, to obtain predicted data corresponding to the first one or more parameters of the first ML application based on the level of similarity between the first ML application and the at least one second ML application being less than the specified threshold
(paragraph [0017]) “For example, a similarity threshold may be implemented to identify a similarity of data between a first data set used to generate a first model and a new data set that is received by the system. When the similarity between these two data sets is meets or exceeds the similarity threshold, the system may reuse a model with the highest similarity value. If the similarity threshold is not met/exceeded, the system may train an existing model with the new data set and compute an accuracy value.”
Tsay, Vo, Kallianpur, and the instant application are analogous because they are all directed to machine learning.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the present invention to modify the machine learning sharing as taught by Tsay in view of Vo by training a ML application based on the level of similarity being less than the threshold as taught by Kallianpur because (Kallianpur paragraph [0013]) “However, rather than training the first model for every new “k” instances, an improved process of training this model may involve reusing past models. Unfortunately, when data change, the model (i.e., a past model) may no longer be able to generate an accurate prediction for the new dataset.” That is, when existing models are not sufficiently relevant to the task at hand, it’s pointless to reuse them and a new model must be trained.
Regarding Claim 13
Claim 13 is a computer apparatus claim corresponding to method claim 6. The only difference is that claim 13 recites a computer and memory as taught in the rejection of claim 8 above. The remaining limitations of the claim are taught by the rejection of claim 6.
Regarding Claim 20
Claim 20 is a non-transitory computer-readable medium claim corresponding to method claim 6. The only difference is that claim 20 recites a computer-readable medium as taught in the rejection of claim 15 above. The remaining limitations of the claim are taught by the rejection of claim 6.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Zhao et al. “Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform” teaches a platform for sharing machine learning models over a network.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ezra J Baker whose telephone number is (703)756-1087. The examiner can normally be reached Monday - Friday 10:00 am - 8:00 pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Yi can be reached at (571) 270-7519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.J.B./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
Prosecution Insights