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 .
DETAILED ACTION
This action is in response to the application filed 23 February 2024. Claims 1-20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10 February 2025 is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 2-5, 9-12, and 16-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims 1, 8, and 15, and dependent Claims 6, 7, 13, 14, and 20, are held to be eligible as they do not recite any abstract ideas. Claims 2-5, 9-12, and 16-19 do recite abstract ideas and do not recite any additional elements that amount to integration of the abstract idea into a practical application or significantly more, thus are ineligible. Below is a full analysis of the rejected claims.
Regarding Claim 2
Step 1
Claim 2 recites a data processing method, and thus the claimed process falls within a statutory category of invention.
Step 2A Prong 1
The claim recites determining a first class group based on the application scenario feature of the data collected by the first edge device, wherein the first class group comprises the first edge device, and data of edge devices in the first class group is identical or similar to the data of the application scenario feature, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element obtaining a first training set based on an application scenario feature of data collected by a first edge device, wherein the first training set comprises the data collected by the first edge device and sample data associated with the application scenario feature (as recited by Claim 1) amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional element obtaining a trained first neural network based on the first training set and a first neural network deployed on the first edge device (as recited by Claim 1) amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional element deploying the trained first neural network on the first edge device (as recited by Claim 1) invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element obtaining the first training set based on the data of the edge devices in the first class group amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering").
Step 2B
The additional element obtaining a first training set based on an application scenario feature of data collected by a first edge device, wherein the first training set comprises the data collected by the first edge device and sample data associated with the application scenario feature (as recited by Claim 1) is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network"). The additional element obtaining a trained first neural network based on the first training set and a first neural network deployed on the first edge device (as recited by Claim 1) is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network"). The additional element deploying the trained first neural network on the first edge device (as recited by Claim 1) invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it"). The additional element obtaining the first training set based on the data of the edge devices in the first class group is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 3
Step 1
Regarding Claim 3, the rejection of Claim 2 is incorporated.
Step 2A Prong 1
The claim recites determining K edge devices in the first class group based on device similarity between the edge devices in the first class group, wherein the similarity indicates a degree of similarity between application scenario features of data of two edge devices, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element obtaining the first training set based on the data of the first edge device and data of the K edge devices in the first class group amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering").
Step 2B
The additional element obtaining the first training set based on the data of the first edge device and data of the K edge devices in the first class group is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 4
Step 1
Claim 4 recites a data processing method, and thus the claimed process falls within a statutory category of invention.
Step 2A Prong 1
The claim recites classifying edge devices in a data processing system into a plurality of class groups based on a classification policy, wherein the classification policy indicates a classification rule based on device similarity between the edge devices, the plurality of class groups comprises the first class group, and each class group comprises multiple edge devices, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element obtaining a first training set based on an application scenario feature of data collected by a first edge device, wherein the first training set comprises the data collected by the first edge device and sample data associated with the application scenario feature (as recited by Claim 1) amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional element obtaining a trained first neural network based on the first training set and a first neural network deployed on the first edge device (as recited by Claim 1) amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional element deploying the trained first neural network on the first edge device (as recited by Claim 1) invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
Step 2B
The additional element obtaining a first training set based on an application scenario feature of data collected by a first edge device, wherein the first training set comprises the data collected by the first edge device and sample data associated with the application scenario feature (as recited by Claim 1) is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network"). The additional element obtaining a trained first neural network based on the first training set and a first neural network deployed on the first edge device (as recited by Claim 1) is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network"). The additional element deploying the trained first neural network on the first edge device (as recited by Claim 1) invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Regarding Claim 5
Step 1
Regarding Claim 5, the rejection of Claim 4 is incorporated.
Step 2A Prong 1
The claim recites determining the device similarity between the edge devices based on an application scenario feature of data of the edge devices in the data processing system in a preset time period, which is a mental process.
Thus, the claim recites an abstract idea.
Step 2A Prong 2
The additional element obtaining a first training set based on an application scenario feature of data collected by a first edge device, wherein the first training set comprises the data collected by the first edge device and sample data associated with the application scenario feature (as recited by Claim 1) amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional element obtaining a trained first neural network based on the first training set and a first neural network deployed on the first edge device (as recited by Claim 1) amounts to insignificant extra-solution activity (see MPEP 2106.05(g), "mere data gathering"). The additional element deploying the trained first neural network on the first edge device (as recited by Claim 1) invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
Step 2B
The additional element obtaining a first training set based on an application scenario feature of data collected by a first edge device, wherein the first training set comprises the data collected by the first edge device and sample data associated with the application scenario feature (as recited by Claim 1) is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network"). The additional element obtaining a trained first neural network based on the first training set and a first neural network deployed on the first edge device (as recited by Claim 1) is well-understood, routine, conventional activity (see MPEP 2106.05(d), "receiving or transmitting data over a network"). The additional element deploying the trained first neural network on the first edge device (as recited by Claim 1) invokes a computer or other machinery merely as a tool to perform an existing process (see MPEP 2106.05(f), "apply it").
The claim lacks additional elements that integrate it into a practical application or provide significantly more, so it is directed to an abstract idea and is ineligible.
Claims 9-12 incorporate substantively all the limitations of Claims 2-5, respectively, in device form and are rejected under the same rationales.
Claims 16-19, incorporate substantively all the limitations of Claims 2-5, respectively, in device form and are rejected under the same rationales.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8-13, and 15-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Baughman, et al. (US 2022/0101174 A1, hereinafter "Baughman").
Regarding Claim 1, Baughman teaches:
A data processing method performed by a data processing device (Baughman, Claim 1: "A method for managing a machine learning algorithm, the method comprising: receiving, by a first device of a plurality of computing devices, a first algorithm; and updating the first algorithm, to create a second algorithm, by the first device of the plurality of computing devices"), comprising:
obtaining a first training set based on an application scenario feature of data collected by a first edge device (Baughman, [0040]: "At step S450, edge cluster program transmits updated information to a node in the cloud. ... [T]he updated information may be non-PII 118," where Baughman's non-PII corresponds to training data, as in [0033]: "The cloud model 122 may be initially trained using non-PII 128, which may be aggregated from non-PII 118 located on each edge device" and corresponds to feature data, as in [0042]: "the cloud model 122 may be trained for speech recognition. In one example of this use case, each edge cluster may be defined by geography, and thus the edge model 112 may account for regional variations in dialect or usage"), wherein the first training set comprises the data collected by the first edge device and sample data associated with the application scenario feature (Baughman, [0029]: "Non-PII 118 may be any additional information that is relevant to edge model 112 or cloud model 114 but does not contain sensitive information. Such information may include choices or decisions made by a user of edge device 110 with respect to the model, as well as any generic information that might have predictive value in edge model 112 or cloud model 114," where Baughman's training data comprises sample data, as in [0002]: "Machine learning algorithms build a mathematical model based on sample data, known as 'training data', in order to make predictions or decisions without being explicitly programmed to do so");
obtaining a trained first neural network based on the first training set and a first neural network deployed on the first edge device (Baughman, [0033]: "At step S310, the cloud model 122 is created on the cloud 120 (e.g., from AI-Node 120-6). ... The cloud model 122 may be initially trained using non-PII 128, which may be aggregated from non-PII 118 located on each edge device" and [0021]: "Cloud model 122 may be an Artificial Intelligence (AI) or Machine Leaming (ML) model trained on a collection of global data to solve a particular problem. Cloud model 122 may use AI algorithms such as ... Neural Networks .... [C]loud model 122 may be initially trained on a global set of information but may be subsequently updated based on updated model parameters of different edge models 112 received from edge devices 110"); and
deploying the trained first neural network on the first edge device (Baughman, Fig. 3, S320, "Deploy Global Model to Edge Devices" and [0033]: "FIG. 3 is a flow chart illustrating a method of deployment of the cloud model 122 to edge devices. At step S310, the cloud model 122 is created on the cloud 120 .... The cloud model 122 may be initially trained using non-PII 128" and [0034]: "At step S320, the cloud model 122 may be deployed .... At this stage, the cloud model 122 being deployed to each edge device 110 is identical").
Regarding Claim 8, Baughman teaches:
A data processing device comprising: a memory storing executable instructions; and a processor configured to executable the executable instructions (Baughman, [0047] The edge cluster program 111, edge model 112, cloud model 114, ... are stored in persistent storage 908 for execution by one or more of the respective computer processors 904 via one or more memories of memory .... that is capable of storing program instructions or digital information") to: perform precisely those steps recited by the method of Claim 1. Claim 8 is rejected under the same rationale as Claim 1.
Regarding Claim 15, Baughman teaches:
A non-transitory computer-readable storage medium having stored thereon executable instructions that, when executed by a processor of a data processing device, cause the data processing device (Baughman, [0079]: "The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. ... A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se") to: perform precisely those steps recited by the method of Claim 1. Claim 15 is rejected under the same rationale as Claim 1.
Regarding Claim 2, the rejection of Claim 1 is incorporated. Baughman teaches:
wherein the step of obtaining the first training set comprises: determining a first class group based on the application scenario feature of the data collected by the first edge device, wherein the first class group comprises the first edge device, and data of edge devices in the first class group is identical or similar to the data of the application scenario feature (Baughman, [0031]: "a first edge cluster 200 may contain one or more edge devices such as edge device 110-1, edge device 110-2, edge device 110-3, and edge device 110-4. ... In the depicted embodiment, edge devices of the first edge cluster 200 may share non-PII 128, or alternatively may share model parameters, such that the edge model 112 for each device of the first edge cluster 200 is substantially similar or identical" and [0032]: "the boundary for an edge cluster, such as the first edge cluster 200 and the second edge cluster 210, may be set based on the problem space, using variables that have a high degree of model correlation between users. For example, in a context of Fantasy Football, the favorite team of users may suggest a high degree of similarity amongst the group of users, and thus may for a criterion for inclusion into an edge cluster"); and
obtaining the first training set based on the data of the edge devices in the first class group (Baughman, [0040]: "At step S450, edge cluster program transmits updated information to a node in the cloud. ... [T]he updated information may be non-PII 118.... The transmission of the updated information may be performed ... to coordinate transmission of non-PII 118 and model hyperparameters 124 of edge model 112 back to the cloud 120").
Regarding Claim 3, the rejection of Claim 2 is incorporated. Baughman teaches:
wherein the step of obtaining the first training set based on the data of the edge devices in the first class group comprises: determining K edge devices in the first class group based on device similarity between the edge devices in the first class group, wherein the similarity indicates a degree of similarity between application scenario features of data of two edge devices (Baughman, [0031]: "a first edge cluster 200 may contain one or more edge devices such as edge device 110-1, edge device 110-2, edge device 110-3, and edge device 110-4. ... In the depicted embodiment, edge devices of the first edge cluster 200 may share non-PII 128, or alternatively may share model parameters, such that the edge model 112 for each device of the first edge cluster 200 is substantially similar or identical" and [0032]: "the boundary for an edge cluster, such as the first edge cluster 200 and the second edge cluster 210, may be set based on the problem space, using variables that have a high degree of model correlation between users. For example, in a context of Fantasy Football, the favorite team of users may suggest a high degree of similarity amongst the group of users, and thus may for a criterion for inclusion into an edge cluster," where Baughman's clustering of devices may be done according to a K-means algorithm, as in Claim 3: "The method of claim 1, wherein the first algorithm is a K-nearest neighbor algorithm"); and
obtaining the first training set based on the data of the first edge device and data of the K edge devices in the first class group (Baughman, [0040]: "At step S450, edge cluster program transmits updated information to a node in the cloud. ... [T]he updated information may be non-PII 118.... The transmission of the updated information may be performed ... to coordinate transmission of non-PII 118 and model hyperparameters 124 of edge model 112 back to the cloud 120").
Regarding Claim 4, the rejection of Claim 1 is incorporated. Baughman teaches:
wherein before obtaining the first training set, the method further comprises: classifying edge devices in a data processing system into a plurality of class groups based on a classification policy, wherein the classification policy indicates a classification rule based on device similarity between the edge devices (Baughman, [0031]: "edge devices of the first edge cluster 200 may share non-PII 128, or alternatively may share model parameters, such that the edge model 112 for each device of the first edge cluster 200 is substantially similar or identical" and [0032]: "the boundary for an edge cluster, such as the first edge cluster 200 and the second edge cluster 210, may be set based on the problem space, using variables that have a high degree of model correlation between users. For example, in a context of Fantasy Football, the favorite team of users may suggest a high degree of similarity amongst the group of users, and thus may for a criterion for inclusion into an edge cluster," where Baughman's criterion for inclusion corresponds to the instant policy), the plurality of class groups comprises the first class group (Baughman, Fig. 2, depicting First Edge Cluster 200 and Second Edge Cluster 210), and each class group comprises multiple edge devices (Baughman, [0031]: "a first edge cluster 200 may contain one or more edge devices such as edge device 110-1, edge device 110-2, edge device 110-3, and edge device 110-4").
Regarding Claim 5, the rejection of Claim 4 is incorporated. Baughman teaches:
determining the device similarity between the edge devices based on an application scenario feature of data of the edge devices in the data processing system in a preset time period (Baughman, [0041]: "The initial cloud model 122 may be trained using scores and statistics from previous Football seasons to create a predictive model for predictive scores for a player for any given matchup. The cloud model 122 may use these predictive scores to assign values to such players in order to aid in comparing players for drafting, trades or lineup selection. The cloud model 122 may be released to a plurality of edge devices, which update the original cloud model 122 to edge model 112 based on actions performed by users in the group. In one example of this use case, each edge cluster may be defined by geography," where Baughman's user clustering by geography according to the sports season corresponds to the instant in a preset time period).
Regarding Claim 6, the rejection of Claim 1 is incorporated. XXX teaches:
wherein the application scenario feature comprises a light feature, a texture feature, a shape feature, or a spatial relationship feature (Baughman, [0042]: "each edge cluster may be defined by geography, and thus the edge model 112 may account for regional variations in dialect or usage," where Baughman's geographic regions correspond to spatial relationship features).
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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Baughman, et al. (US 2022/0101174 A1, hereinafter "Baughman") in view of Rogers, et al. (US 2019/0147297 A1, hereinafter "Rogers") in view of Yoshiyama, et al. (US 2018/0330237 A1, hereinafter "Yoshiyama").
Regarding Claim 7, the rejection of Claim 1 is incorporated.
Baughman teaches a first class group that is based on the application scenario feature of the data.
Baughman does not explicitly teach displaying the first class group.
However, Rogers teaches:
displaying the first class group (Rogers, [0016]: "as labeled training data is used to remap the projection, users are able to visually evaluate the selectivity of the model-data combination that is being merged. If clusters or biasing in the map is observed to be associated with each of the classes, then the training data is a good candidate for the ML model. If the labeled data remains randomly distributed in the data map even after much of the data is labeled, then the training data is not compatible with the ML model").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Baughman regarding a first class group that is based on the application scenario feature of the data with those of Rogers regarding displaying the first class group.
The motivation to do so would be to facilitate user evaluation of training data and a model (Rogers, [0016]: "as labeled training data is used to remap the projection, users are able to visually evaluate the selectivity of the model-data combination that is being merged. If clusters or biasing in the map is observed to be associated with each of the classes, then the training data is a good candidate for the ML model. If the labeled data remains randomly distributed in the data map even after much of the data is labeled, then the training data is not compatible with the ML model").
The Baughman/Rogers combination teaches obtaining a trained first neural network.
The Baughman/Rogers combination does not explicitly teach displaying ... the trained ... neural network.
However, Yoshiyama teaches:
displaying ... the trained ... neural network (Yoshiyama, [0067]: "The network structure of the neural network displayed in the palette P2 according to the present embodiment has been described above. As described above, as a result of the network structure of the selected neural network being displayed in the palette P2, the user can confirm the learning result and the network structure on one screen. By this means, the user can compare learning results by a plurality of models of neural networks in more details").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the Baughman/Rogers combination regarding obtaining a trained first neural network with those of Yoshiyama regarding displaying the trained neural network.
The motivation to do so would be to facilitate user confirmation of a network structure (Yoshiyama, [0067]: "The network structure of the neural network displayed in the palette P2 according to the present embodiment has been described above. As described above, as a result of the network structure of the selected neural network being displayed in the palette P2, the user can confirm the learning result and the network structure on one screen. By this means, the user can compare learning results by a plurality of models of neural networks in more details").
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT N DAY whose telephone number is (703)756-1519. The examiner can normally be reached M-F 9-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kakali Chaki can be reached at (571) 272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.N.D./Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122
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