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
Status of Claims
This action is in reply to the amendments filed on August 14, 2026.
This application claims priority to CN202011095961.7, filed on October 14, 2020.
Claims 1, 2, and 3-10 are currently pending.
Claims 1, 4, 8, and 10 have been amended.
Claim 3 has been cancelled.
Specification
The previous objection to the specification is withdrawn in view of Applicant’s amendments.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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: “an acquisition unit configured to…,” “a training unit configured to…,” a storage unit configured to…,” and “a storage unit configured to…” in claim 8.
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 § 101
The previous rejection of claim 10, under 35 U.S.C. 101, is withdrawn in view of Applicant’s amendments and arguments; see page 6 of remarks.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cai et al., U.S. Patent Application Publication 2019/0347282 (“Cai”).
With respect to independent claim 8, Cai teaches:
A combined-learning-based Internet of Things data service apparatus (The platform, described throughout Cai, can be enabled with Internet of Things devices; see [0140].), comprising a processor (Cai teaches a processor in figure 1 and [0142].), wherein the processor implements any of the following units:
an acquisition unit configured to acquire a data processing result of an edge side for target user data (Cai teaches gathering information from a variety of network locations including a user interface application located on a user device; see figure 2 , [0074], [0127], [0129]. [0127] notes that the GUI interface application can create workflows (processing result) and that the computing components can transform diverse unstructured data into actionable insights (processing result).);
a training unit configured to perform combined learning training based on a combined learning engine (Cai teaches combining machine learning and deep learning in [0148]. Figure 17 of Cai discloses a Model Building/Training state that trains a feedback box model to perform natural language processing, sentiment analysis, using user comment samples as training data.), the data processing result and the target user data (Cai teaches gathering information from a variety of network locations including a user interface application located on a user device; see figure 2 , [0074], [0127], [0129].), to obtain a combined learning training model (Cai teaches combining machine learning and deep learning in [0148].);
a storage unit configured to store the combined learning training model in a target model base (Cai teaches a platform with a processor and a memory storing machine executable instructions and prescriptive models in [0026].); and
a call unit configured to call a service-side requirement by using the target model base (Cai teaches implementing a platform comprising an artificial intelligence process in at least [0075]. The claim does not detail the call, service-side requirement, or how the target model base is used in the call. Cai’s teaching of implementing a model would require a “call” in order for the model to be used, and is sufficient to teach the limitation.).
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.
Claim(s) 1, 2, 4-7, 9, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cai et al., U.S. Patent Application Publication 2019/0347282 (“Cai”); in view of Li, et al., U.S. Patent Application Publication 2023/0109389 (“Li”), and in further view of Xu et al., “CrpytoNN: Training Neural Networks over Encrypted Data” (“Xu”).
With respect to independent claim 1, Cai teaches:
A combined-learning-based Internet of Things data service method (The platform, described throughout Cai, can be enabled with Internet of Things devices; see [0140].), comprising:
acquiring a data processing result of an edge side for target user data (Cai teaches gathering information from a variety of network locations including a user interface application located on a user device; see figure 2 , [0074], [0127], [0129]. [0127] notes that the GUI interface application can create workflows (processing result) and that the computing components can transform diverse unstructured data into actionable insights (processing result).);
performing combined learning training based on a combined learning engine (Cai teaches combining machine learning and deep learning in [0148].), the data processing result and the target user data (Cai teaches gathering information from a variety of network locations including a user interface application located on a user device; see figure 2 , [0074], [0127], [0129].), to obtain a combined learning training model (Cai teaches combining machine learning and deep learning in [0148].);
storing the combined learning training model in a target model base (Cai teaches a platform with a processor and a memory storing machine executable instructions and prescriptive models in [0026].); and
calling a service-side requirement by using the target model base (Cai teaches implementing a platform comprising an artificial intelligence process in at least [0075]. The claim does not detail the call, service-side requirement, or how the target model base is used in the call. Cai’s teaching of implementing a model would require a “call” in order for the model to be used, and is sufficient to teach the limitation.),
wherein the step of performing combined learning training based on a combined learning engine, the data processing result and the target user data (Cai teaches this in the rejection of claim 1 above.), to obtain a combined learning training model comprises:
…
integrating an objective machine learning algorithm and an objective deep learning algorithm into the combined learning engine (Cai teaches combining machine learning and deep learning in [0148].);
…
performing combined learning training on the initial model by using the training sample set and the combined learning engine, to obtain the combined learning training model (Cai teaches combining machine learning and deep learning in [0148]. Figure 17 of Cai discloses a Model Building/Training state that trains a feedback box model to perform natural language processing, sentiment analysis, using user comment samples as training data.).
Cai does not explicitly disclose:
acquiring an initial model;
adding the data processing result and the target user data to a sample set, to obtain a sample set after data addition;
encrypting data in the sample set after data addition to obtain an encrypted sample set as a training sample set for training the initial model; and
However, Li teaches:
acquiring an initial model (Li teaches training various models in [0059], including models that may be implemented as hardware, software, or a combination of hardware and software. At least hardware models would require an initial model before training, and, generally, in order to train a model there must first be an initial model.);
adding the data processing result and the target user data to a sample set, to obtain a sample set after data addition (Li teaches encapsulating a payload having the user plane data into an IP packet in [0008]. What is it being encapsulated with?);
Cai and Li are analogous art directed towards distributed machine learning implementation. Cai teaches an incident management system that uses AI across a network of devices to predict events, and Li teaches secure transmission methods for transmitting data across devices for use in machine learning.
It would have been obvious for one of ordinary skill in distributed machine learning to incorporate Li’s teaching of encryption and training into Cai’s system at the time of filing. It would have been obvious because one of ordinary skill would be motivated to keep user data secure by encrypting the data before transferring; see [0007].
Cai and Li do not explicitly disclose:
encrypting data in the sample set after data addition to obtain an encrypted sample set as a training sample set for training the initial model; and
However, Xu teaches this limitation:
encrypting data in the sample set after data addition to obtain an encrypted sample set as a training sample set for training the initial model (Xu teaches training neural networks over encrypted data in the abstract, and in section III, in particular III.B.); and
Cai, Li, and Xu are analogous art directed towards machine learning implementation. Cai teaches an incident management system that uses AI across a network of devices to predict events, Li teaches secure transmission methods for transmitting data across devices for use in machine learning, and Xu teaches training neural networks over encrypted data to maintain data security.
It would have been obvious for one of ordinary skill in machine learning to incorporate Xu’s teaching of encryption and training into Cai’s system at the time of filing. It would have been obvious because one of ordinary skill would be motivated to keep user data secure by encrypting the data before transferring; see the abstract.
With respect to dependent claim 2, the rejection of claim 1 is incorporated. Further, Cai teaches:
wherein, after the step of acquiring a data processing result of an edge side for target user data, the method further comprises:
performing data asset management on the data processing result, wherein the data asset management comprises at least one of the following: metadata management (Cai teaches an interface platform for extracting network metadata and identifying relationships or links between infrastructure components for an application in [0036].), data asset storage (Cai teaches the platform includes a data storage in [0076].), data quality management (The claim requires only “one or more” of the elements listed.), data authorization and delivery management (The claim requires only “one or more” of the elements listed.), and data security management (Cai teaches an interface that includes an overall security compliance indicia to trigger the display of visual elements for security compliance reporting for an application server or database in [0154].).
With respect to dependent claim 4, the rejection of claim 1 is incorporated. Further, Li teaches:
wherein a training sample in the training sample set comprises sample input data and sample output data, and the combined learning training model is trained by taking the sample input data as input and the sample output data as expected output (Li teaches models may be trained as supervised learning models in [0059].) (Cai teaches a Model Evaluation/Monitoring state in figure 17 that compares model output with actual values.).
See the rejection of claim 1 for the motivation to combine references.
With respect to dependent claim 5, the rejection of claim 1 is incorporated. Further, Cai teaches:
wherein the step of storing the combined learning training model in a target model base comprises:
encapsulating the combined learning training model to obtain an encapsulated combined learning training model (Cai teaches combining machine learning and deep learning in [0148]. The “encapsulating” is not defined and combining multiple machine learning models is an encapsulation of the models.);
generating an interface of the encapsulated combined learning training model, wherein the interface comprises: a management interface (Cai teaches an IT incident management platform that can generate visual elements for display at an interactive interface; see abstract, figure 1, and corresponding discussion of figure 1 in the specification, beginning at [0069].) and a call interface (Cai teaches an interface that includes a Change Request Potential Impact panel with visual elements for change requests that can potentially impact a given application environment; see [0157]-[00158]. The call and call interface are not detailed and a change request is a call.); and
storing the encapsulated combined learning training model to the target model base in response to determining completion of generation of the interface (Cai teaches a platform with a processor and a memory storing machine executable instructions and prescriptive models in [0026].).
With respect to dependent claim 6, the rejection of claim 5 is incorporated. Further, Cai teaches:
wherein the method further comprises:
acquiring a management instruction in response to detecting a management request from a target management user, wherein the management instruction comprises an interface and management content of a managed model (Cai teaches an IT incident management platform that can generate visual elements for display at an interactive interface; see abstract, figure 1, and corresponding discussion of figure 1 in the specification, beginning at [0069]. The management platform can enable predictive models (managed model); see abstract.); and
processing, based on the management instruction, models in the target model base whose interfaces are the same as the interface of the managed model (Cai teaches a consistent incident management platform in figure 1 and beginning at [0069] if the specification. Cai teaches a single interface and multiple prescriptive models; see abstract and [0072]. Therefore, all of the models will have the same interface.).
With respect to dependent claim 7, the rejection of claim 5 is incorporated. Further, Cai teaches:
wherein the method further comprises:
acquiring the call interface in response to detecting a call request from a target user (Cai teaches an interface that includes a Change Request Potential Impact panel with visual elements for change requests that can potentially impact a given application environment and that comprises a search field to search; see [0157]-[00158]. The requests and searches would be input by a user. The claim does not detail the target user and any user using the panel taught by Cai can be a target user.);
extracting, from the target model base, a model whose interface is the same as the call interface (Cai teaches an interface that includes a Change Request Potential Impact panel with visual elements for change requests that can potentially impact a given application environment; see [0157]-[00158]. Cai teaches a single Change Request Potential Impact panel interface and multiple prescriptive models; see abstract and [0072]. Therefore, all of the models will have the same call interface.); and
performing, in response to detecting a combined training request from the target user, combined training on the extracted model and at least one model stored by a terminal device of the target user (Cai teaches a user device that can run models in [0129] and implementing a trained neural network regression model in [0182]. In order to implement a trained neural network model the model must be trained.).
With respect to dependent claim 9, the rejection of claim 1 is incorporated. Further, Cai teaches:
An electronic device, comprising:
one or more processors (Cai teaches processor implementation in [0246].); and
a storage apparatus storing one or more programs (Cai teaches data storage for program code in [0246]-[0247].);
the one or more programs, when executed by the one or more processors, causing the one or more processors to perform the method according to claim 1 (Cai teaches a computer implement system that would require code to function. See the rejection of claim 1 above.).
With respect to dependent claim 10, the rejection of claim 1 is incorporated. Further, Cai teaches:
A non-transitory computer-readable storage medium, storing a computer program, wherein, when the program is executed by a processor, the method according to claim 1 is performed (Cai teaches storage mediums to implement the disclosed methods in [0248]-[0249]. See the rejection of claim 1 above.).
Response to Arguments
Applicant's arguments filed August 14, 2026 have been fully considered but they are not persuasive.
Applicant’s amended specification is accepted. Arguments on page 6 are persuasive.
The “units” in amended claim 6 are still being interpreted under 35 U.S.C. 112(f), as indicated above.
Also, on page 6, Applicant argues that the previous rejection of claim 10 under 35 U.S.C. 101 is overcome in view of the amendments. Examiner agrees and the rejection is withdrawn.
On page 7, Applicant argues that the cited art does not teach amended claim 1. The rejection of claim 1 has been modified in view of Applicant’s amendment. Xu has been incorporated into the rejection above to teach training a neural network using encrypted data, as argued on page 7.
Prior Art of Record
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Nandakumar et al., “Towards Deep Neural Network Training on Encrypted Data” – teaches training deep neural networks using encrypted training data.
Conclusion
Claims 1, 2, and 4-10 are rejected.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL T PELLETT whose telephone number is (571)270-7156. The examiner can normally be reached on Monday - Friday 9-5 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached on 571-272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL T PELLETT/Primary Examiner, Art Unit 2121
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