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
Response to Arguments
Applicant's arguments with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments are directed to the amended subject matter; new prior art is provided below.
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve model usage for developers to recommended development options can be recommended more accurately supported by the specification, and reflected by the claims e.g. in spec: 0022. In other words, the claims enable the invention to increase accuracy and efficiency for developers/users such that the appropriate knowledge references can be automatically recommended to technical support engineers, who process user service requests, more accurately than using a universal model, so that the engineers can quickly obtain information that helps to process request content described in specific request messages. As a result, the efficiency of processing request message from users can be improved, and the satisfaction degree of the users can be maintained at a good level.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
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, 4-9, 12-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220255885 A1 Aharoni; Asaf et al. (hereinafter Aharoni) in view of US 12488798 B1 Eakin; Aaron et al. (hereinafter Eakin) and further in view of US 20210326197 A1 Honnappa; Vidyasagar et al. (hereinafter Honnappa).
1. A method for processing a request message, comprising: receiving a request message from a user, wherein the request message describes a user request to be processed; (handling a user request as in 321A2 in fig. 3a-3e)
determining a text feature representation of the request message by using a trained text feature representation model, wherein the text feature representation model is trained by using a general corpus and a set of historical request messages; and (ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and ML models fig. 2 0043 and 0086 to provide a recommendation for a developer to select 0080, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
determining, based on the text feature representation, one or more entries associated with the request message… (provide a recommendation for a developer to select 0080, ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
However, while Aharoni teaches multiple models with a model per layer of processing as well as ground truth and iterative training, and while LSTM and other concepts are taught for iterative learning, it fails to specify how the multiple models are utilizing from input to output, such as with federated models such as teach student, thus failing to teach:
…in a knowledge base by using a trained reference recommendation model… the reference recommendation model is trained by using a subset of the set of historical request messages, and the request messages in the subset have been associated with the entries in the knowledge base. (Eakin using history with federated models in a supervised context fig. 1 with col 2 lines 47-65, with models illustrated in an iterative learning such as elements 172 and 174 from 180 in fig. 1, supported by col 4 line 45 to col 5 line 56 with col 6 lines 9-32… utilizing labeling/tagging for domain/context in a knowledge base expressly in the federated models col 8 lines 12-32 and fig. 7 with col 32 lines 5-30)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Aharoni to incorporate the above claim limitations as taught by Eakin to allow for combining prior art elements according to known methods to yield predictable results such as using federated models as a form of multiple ML models which allows AI models to learn from decentralized data on user devices, enhancing developer suggestion accuracy while maintaining privacy, which further provides highly personalized, real-time suggestions e.g. predictive text, corrections, or code completion—without transferring sensitive user data to a central server, using the existing features of neural network models.
However, while the combination teaches machine learning to handle user requests, it does not teach the request context of service or IT related inquires as a knowledge base per se:
wherein the knowledge base includes a plurality of entries of technical reference knowledge for resolving user service requests (Honnappa knowledge base 00021 with AI model resolution and learning 0030-0033 with fig. 3-7 to provide recommendations from the model)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Aharoni in view of Eakin to incorporate the above claim limitations as taught by Honnappa to allow for combining prior art elements according to known methods to yield predictable results such as simple substitution of one known element for another to obtain predictable results such as providing context models per application or industry, in this case IT resolution with an AI model or learning model that uses IT requests as the knowledge base, analogous to and substitutable with other context, e.g. product reviews, surveys, forum sentiment, music ranking, etc., thereby provided a multi-context system, but specifically IT requests that learn user needed and resolutions thereof.
Re claim 9, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1, omitting/including hardware for instance, otherwise amounting to a virtually identical scope
For instance, see fig. 1 and fig. 7 which contains the necessary hardware.
Re claim 17, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1, omitting/including hardware for instance, otherwise amounting to a virtually identical scope
For instance, see fig. 1 and fig. 7 which contains the necessary machine instructions per se.
Re claims 4, 12, and 20, Aharoni teaches
4. The method according to claim 1, further comprising training the reference recommendation model, wherein training the reference recommendation model comprises: extracting the subset from a record of the set of historical request messages, and tags corresponding to the request messages in the subset, wherein a corresponding tag of a corresponding request message indicates entries in the knowledge base associated with the corresponding request message; and (labeling or tagging per se as in 0097-0098 and fig. 3a-3e, provide a recommendation for a developer to select 0080, ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
However, while Aharoni teaches multiple models with a model per layer of processing as well as ground truth and iterative training, and while LSTM and other concepts are taught for iterative learning, it fails to specify how the multiple models are utilizing from input to output, such as with federated models such as teach student, thus failing to teach:
training the reference recommendation model by using the subset and the tags. (Eakin using history with federated models in a supervised context fig. 1 with col 2 lines 47-65, with models illustrated in an iterative learning such as elements 172 and 174 from 180 in fig. 1, supported by col 4 line 45 to col 5 line 56 with col 6 lines 9-32… utilizing labeling/tagging for domain/context in a knowledge base expressly in the federated models col 8 lines 12-32 and fig. 7 with col 32 lines 5-30)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Aharoni to incorporate the above claim limitations as taught by Eakin to allow for combining prior art elements according to known methods to yield predictable results such as using federated models as a form of multiple ML models which allows AI models to learn from decentralized data on user devices, enhancing developer suggestion accuracy while maintaining privacy, which further provides highly personalized, real-time suggestions e.g. predictive text, corrections, or code completion—without transferring sensitive user data to a central server, using the existing features of neural network models.
Re claims 5 and 13, Aharoni teaches
5. The method according to claim 4, wherein training the reference recommendation model by using the subset and the tags comprises: determining a set of text feature representations of the subset based on the trained text feature representation model; and (multiple models, provide a recommendation for a developer to select 0080, ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
determining a parameter of the reference recommendation model by using the set of text feature representations and the tags and executing, on the reference recommendation model, a first supervised training task of determining tags based on text feature representations. (multiple models, provide a recommendation for a developer to select 0080, ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
However, while Aharoni teaches multiple models with a model per layer of processing as well as ground truth and iterative training, and while LSTM and other concepts are taught for iterative learning, it fails to specify how the multiple models are utilizing from input to output, such as with federated models such as teach student, thus failing to teach:
the reference recommendation model (sequentially federated)
(Eakin using history with federated models in a supervised context fig. 1 with col 2 lines 47-65, with models illustrated in an iterative learning such as elements 172 and 174 from 180 in fig. 1, supported by col 4 line 45 to col 5 line 56 with col 6 lines 9-32… utilizing labeling/tagging for domain/context in a knowledge base expressly in the federated models col 8 lines 12-32 and fig. 7 with col 32 lines 5-30)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Aharoni to incorporate the above claim limitations as taught by Eakin to allow for combining prior art elements according to known methods to yield predictable results such as using federated models as a form of multiple ML models which allows AI models to learn from decentralized data on user devices, enhancing developer suggestion accuracy while maintaining privacy, which further provides highly personalized, real-time suggestions e.g. predictive text, corrections, or code completion—without transferring sensitive user data to a central server, using the existing features of neural network models.
Re claims 6 and 14, while Aharoni teaches multiple models with a model per layer of processing as well as ground truth and iterative training, and while LSTM and other concepts are taught for iterative learning, it fails to specify how the multiple models are utilizing from input to output, such as with federated models such as teach student, thus failing to teach:
6. The method according to claim 4, wherein training the reference recommendation model by using the subset and the tags comprises: connecting the trained text feature representation model with the reference recommendation model, so that text feature representations output by the text feature representation model are to be input into the reference recommendation model; and (Eakin federated connected e.g. teacher-student, using history with federated models in a supervised context fig. 1 with col 2 lines 47-65, with models illustrated in an iterative learning such as elements 172 and 174 from 180 in fig. 1, supported by col 4 line 45 to col 5 line 56 with col 6 lines 9-32… utilizing labeling/tagging for domain/context in a knowledge base expressly in the federated models col 8 lines 12-32 and fig. 7 with col 32 lines 5-30)
adjusting the parameter of the text feature representation model and determining the parameter of the reference recommendation model by using the subset and the tags and executing, on the connected text feature representation model and reference recommendation model, a second supervised training task of determining tags based on request messages. (Eakin using history with federated models in a supervised context fig. 1 with col 2 lines 47-65, with models illustrated in an iterative learning such as elements 172 and 174 from 180 in fig. 1, supported by col 4 line 45 to col 5 line 56 with col 6 lines 9-32… utilizing labeling/tagging for domain/context in a knowledge base expressly in the federated models col 8 lines 12-32 and fig. 7 with col 32 lines 5-30)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Aharoni to incorporate the above claim limitations as taught by Eakin to allow for combining prior art elements according to known methods to yield predictable results such as using federated models as a form of multiple ML models which allows AI models to learn from decentralized data on user devices, enhancing developer suggestion accuracy while maintaining privacy, which further provides highly personalized, real-time suggestions e.g. predictive text, corrections, or code completion—without transferring sensitive user data to a central server, using the existing features of neural network models.
Re claims 7 and 15, Aharoni teaches7. The method according to claim 1, further comprising:
presenting the one or more entries via a user interface; (display/present a recommendation for a developer to select 0080, ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
receiving a selection for entries in the knowledge base via the user interface; (provide a recommendation for a developer to select 0080, ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
storing a record of the request and the selected entries in an associated manner; and (in memory stored and using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
adjusting the reference recommendation model by using the association between the record and the entries. (fig. 2 all models updated element 170a1, provide a recommendation for a developer to select 0080, ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
However, while Aharoni teaches multiple models with a model per layer of processing as well as ground truth and iterative training, and while LSTM and other concepts are taught for iterative learning, it fails to specify how the multiple models are utilizing from input to output, such as with federated models such as teach student, thus failing to teach:
the reference recommendation model (sequentially federated)
(Eakin using history with federated models in a supervised context fig. 1 with col 2 lines 47-65, with models illustrated in an iterative learning such as elements 172 and 174 from 180 in fig. 1, supported by col 4 line 45 to col 5 line 56 with col 6 lines 9-32… utilizing labeling/tagging for domain/context in a knowledge base expressly in the federated models col 8 lines 12-32 and fig. 7 with col 32 lines 5-30)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Aharoni to incorporate the above claim limitations as taught by Eakin to allow for combining prior art elements according to known methods to yield predictable results such as using federated models as a form of multiple ML models which allows AI models to learn from decentralized data on user devices, enhancing developer suggestion accuracy while maintaining privacy, which further provides highly personalized, real-time suggestions e.g. predictive text, corrections, or code completion—without transferring sensitive user data to a central server, using the existing features of neural network models.
Re claims 8 and 16, Aharoni teaches
8. The method according to claim 1, further comprising:
storing a record of the request and the new entry in an associated manner; and (in memory stored and using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
However, while Aharoni teaches multiple models with a model per layer of processing as well as ground truth and iterative training, and while LSTM and other concepts are taught for iterative learning, it fails to specify how the multiple models are utilizing from input to output, such as with federated models such as teach student, thus failing to teach:
adjusting the reference recommendation model by using the association between the record and the new entry. (Eakin using history with federated models in a supervised context fig. 1 with col 2 lines 47-65, with models illustrated in an iterative learning such as elements 172 and 174 from 180 in fig. 1, supported by col 4 line 45 to col 5 line 56 with col 6 lines 9-32… utilizing labeling/tagging for domain/context in a knowledge base expressly in the federated models col 8 lines 12-32 and fig. 7 with col 32 lines 5-30)
receiving a new entry that should be added into the knowledge base via a user interface; (Eakin using history with federated models in a supervised context fig. 1 with col 2 lines 47-65, with models illustrated in an iterative learning such as elements 172 and 174 from 180 in fig. 1, supported by col 4 line 45 to col 5 line 56 with col 6 lines 9-32… utilizing labeling/tagging for domain/context in a knowledge base expressly in the federated models col 8 lines 12-32 and fig. 7 with col 32 lines 5-30)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Aharoni to incorporate the above claim limitations as taught by Eakin to allow for combining prior art elements according to known methods to yield predictable results such as using federated models as a form of multiple ML models which allows AI models to learn from decentralized data on user devices, enhancing developer suggestion accuracy while maintaining privacy, which further provides highly personalized, real-time suggestions e.g. predictive text, corrections, or code completion—without transferring sensitive user data to a central server, using the existing features of neural network models.
Claims 2, 3, 10, 11, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220255885 A1 Aharoni; Asaf et al. (hereinafter Aharoni) in view of US 12488798 B1 Eakin; Aaron et al. (hereinafter Eakin) and further in view of US 20220012637 A1 REZAZADEGAN TAVAKOLI; Hamed et al. (hereinafter REZAZADEGAN)
Re claims 2, 10, and 18, Aharoni teaches
2. The method according to claim 1, further comprising training the text feature representation model, wherein training the text feature representation model comprises: determining an initial parameter of the text feature representation model by using the general corpus (provide a recommendation for a developer to select 0080, ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
However, while the combination teaches supervised learning and federated models, it fails to teach:
and executing a first unsupervised training task on the text feature representation model. (REZAZADEGAN federated models with unsupervised option 0097, 0107, 0198)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Aharoni in view of Eakin to incorporate the above claim limitations as taught by REZAZADEGAN to allow for combining prior art elements according to known methods to yield predictable results such as adding an unsupervised element to federated models for high-accuracy predictions on known data while identifying emerging trends or anomalies without centralizing sensitive information, in which unsupervised components can leverage vast amounts of unlabeled local data, mitigating the need for expert or SME annotations, where an unsupervised hybrid option of self-supervised using unsupervised, can help pre-train or structure data locally before supervised tuning, helping the global model better handle the high variability of data between clients and requests.
Re claims 3, 11, and 19, Aharoni teaches
3. The method according to claim 2, wherein training the text feature representation model further comprises: extracting the set of historical request messages from a record of the set of historical request messages; and (provide a recommendation for a developer to select 0080, ASR converts audio to text and then using NLU e.g. fig. 1, using previous conversations and a corpus for training 0028 0074 as well as ground truth and multiple ML models fig. 2 0043 and 0086, in the context of developer options when handling a user request as in 321A2 in fig. 3a-3e)
However, while the combination teaches supervised learning and federated models, it fails to teach:
adjusting the initial parameter of the text feature representation model by using the set of request messages (REZAZADEGAN federated models with unsupervised option 0097, 0107, 0198)
and executing a second unsupervised training task on the text feature representation model so as to obtain the trained text feature representation model. (REZAZADEGAN federated models with unsupervised option 0097, 0107, 0198)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Aharoni in view of Eakin to incorporate the above claim limitations as taught by REZAZADEGAN to allow for combining prior art elements according to known methods to yield predictable results such as adding an unsupervised element to federated models for high-accuracy predictions on known data while identifying emerging trends or anomalies without centralizing sensitive information, in which unsupervised components can leverage vast amounts of unlabeled local data, mitigating the need for expert or SME annotations, where an unsupervised hybrid option of self-supervised using unsupervised, can help pre-train or structure data locally before supervised tuning, helping the global model better handle the high variability of data between clients and requests.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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Service request handling
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL COLUCCI whose telephone number is (571)270-1847. The examiner can normally be reached on M-F 9 AM - 7 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Flanders can be reached at (571)272-7516. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
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Michael.Colucci@uspto.gov
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