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 .
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/5/2026 has been entered.
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 1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a system claim. Claim 8 is a method claim. Claim 15 is a system claim. Claim 22 is a CRM claim. Therefore, claims 1, 8, 15, and 22 are directed to either a process, machine, manufacture or composition of matter.
With respect to Claim 1:
Step 2A Prong 1:
processing the obtained plurality of input data using an embedding model to create a vectorized dataset (mental process – user can manually use a model to create a vectorized dataset from the input data)
generating a workflow, by a distributed computational graph, for training a machine learning model (mental process – user can manually generate a workflow for training a machine learning model)
mapping the learned representations to symbolic concepts or rules to create symbolic representations of the learned representations (mental process – user can manually map the learned representations to symbolic concepts or rules to create symbolic representations of the learned representations)
applying symbolic reasoning techniques to the symbolic representations to perform a reasoning task by passing the symbolic representations through data transformation nodes constituting the distributed computational graph (mental process – user can manually apply symbolic reasoning techniques to the symbolic representations to perform a reasoning task)
engineering a prompt using an output of the reasoning task (mental process – user can manually engineer a prompt using an output of the reasoning task)
curating one or more responses for the one or more large language models to create a single response or action (mental process – user can manually curate one or more responses for the one or more large language models to create a single response or action)
Step 2A Prong 2: This judicial exception is not integrated into a practical application. Additional elements:
a plurality of edge devices in communication with a server, the server including one or more hardware processors (mere instructions to apply the exception using a generic computer component)
obtaining a plurality of input data, the input data comprising enterprise knowledge and expert knowledge (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
generating a workflow, by a distributed computational graph, for training a machine learning model (mere instructions to apply the exception using a generic computer component)
training the machine learning model using the vectorized dataset as a training dataset via federated learning at the plurality of edge devices based, in part, on local knowledge bases associated with each of the plurality of edge devices, wherein the machine learning model is configured to learn representations of the vectorized dataset (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
applying symbolic reasoning techniques to the symbolic representations to perform a reasoning task by passing the symbolic representations through data transformation nodes constituting the distributed computational graph (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
submitting the prompt and a plurality of context elements to one or more large language models (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
transmitting the single response or action to a user device (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additional elements:
a plurality of edge devices in communication with a server, the server including one or more hardware processors (mere instructions to apply the exception using a generic computer component)
obtaining a plurality of input data, the input data comprising enterprise knowledge and expert knowledge (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer)
generating a workflow, by a distributed computational graph, for training a machine learning model (mere instructions to apply the exception using a generic computer component)
training the machine learning model using the vectorized dataset as a training dataset via federated learning at the plurality of edge devices based, in part, on local knowledge bases associated with each of the plurality of edge devices, wherein the machine learning model is configured to learn representations of the vectorized dataset (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: high level recitation of training a machine learning model with previously determined data)
applying symbolic reasoning techniques to the symbolic representations to perform a reasoning task by passing the symbolic representations through data transformation nodes constituting the distributed computational graph (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g))
submitting the prompt and a plurality of context elements to one or more large language models (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer)
transmitting the single response or action to a user device (MPEP 2106.05(d)(II) indicate that merely “storing and retrieving information in memory” or “receiving or transmitting data over a network” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed storing step is well-understood, routine, conventional activity is supported under Berkheimer)
Conclusion: The claim is not patent eligible.
Claims 8, 15, and 22 are rejected on the same grounds as claim 1. Additionally, claim 15 discloses one or more computers with executable instructions, and claim 22 discloses non-transitory, computer-readable storage media having computer executable instructions. Both of these additional elements are mere instructions to apply the exception using a generic computer component under Step 2A prong 2 and Step 2B.
Regarding Claim 2, 9, 16, 23: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually include wherein the plurality of context elements comprises retrieval-augmented generation (RAG) information.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 3, 10, 17, 24: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually include wherein the RAG information is obtained from a RAG marketplace.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 4, 11, 18, 25: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually include wherein the plurality of context elements comprises information associated with the user device.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 5, 12, 19, 26: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually include wherein the plurality of context elements comprises information associated with an action a user of the user device is performing during interaction with the platform.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 6, 13, 20, 27: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually include wherein the expert knowledge comprises scored datasets or scored model output.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Regarding Claim 7, 14, 21, 28: The limitation(s), as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitation(s) in the mind. That is, nothing in the claim limitation(s) precludes the step from practically being performed in the mind.
The limitation(s) encompasses the user manually include wherein the expert knowledge is obtained from an expert knowledge marketplace.
These judicial exceptions are not integrated into a practical application. In particular, the claims do not recite any additional elements. Accordingly, this does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, no additional elements are cited. Accordingly, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-5, 8-12, 15-19, 22-26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amazon, What is RAG? in view of Zhang et al. (hereinafter Zhang), A survey on federated learning, further in view of Futia et al. (hereinafter Futia), On the Integration of Knowledge Graphs into Deep Learning Models for a More Comprehensible AI—Three Challenges for Future Research.
Regarding Claim 1, Amazon discloses a computing system for operationalizing generative artificial intelligence employing a distributed neuro-symbolic reasoning and action platform, the computing system comprising:
obtaining a plurality of input data, the input data comprising enterprise knowledge and expert knowledge ["specific domains or an organization's internal knowledge base" pg. 2; "external data. It can come from multiple data sources, such as a APIs, databases, or document repositories" pg. 3; "add vast external knowledge sources" pg. 4; "Modern enterprises store vast amounts of information like manuals, FAQs, research reports, customer service guides, and human resource document repositories across various systems" pg. 4];
processing the obtained plurality of input data using an embedding model to create a vectorized dataset ["embedding language models, converts data into numerical representations and stores it in a vector database" pg. 3];
training the machine learning model using the vectorized dataset as a training dataset via federated learning at the plurality of edge devices based, in part, on local knowledge bases associated with each of the plurality of edge devices, wherein the machine learning model is configured to learn representations of the vectorized dataset ["embedding language models, converts data into numerical representations and stores it in a vector database" pg. 3];
engineering a prompt ["prompt engineering techniques" pg. 3] using an output of the reasoning task;
submitting the prompt and a plurality of context elements to one or more large language models [#4 in the figure on pg. 4; “RAG model augments the user input (or prompts) by adding the relevant retrieved data in context.” pg. 3 ¶5];
curating one or more responses for the one or more large language models to create a single response or action [#5 in the figure on pg. 4]; and
transmitting the single response or action to a user device [#5 in the figure on pg. 4].
However, Amazon fails to explicitly disclose a plurality of edge devices in communication with a server, the server including one or more hardware processors configured for:
generating a workflow, by a distributed computational graph, for training a machine learning model;
training the machine learning model using the vectorized dataset as a training dataset via federated learning at the plurality of edge devices based, in part, on local knowledge bases associated with each of the plurality of edge devices, wherein the machine learning model is configured to learn representations of the vectorized dataset;
applying symbolic reasoning techniques to the symbolic representations to perform a reasoning task by passing the symbolic representations through data transformation nodes constituting the distributed computational graph.
Zhang discloses a plurality of edge devices in communication with a server, the server including one or more hardware processors [“multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggregator” Abstract; Fig. 1] configured for:
generating a workflow, by a distributed computational graph, for training a machine learning model [“schematic diagram of federated learning” Fig. 1];
training the machine learning model using the vectorized dataset as a training dataset via federated learning at the plurality of edge devices based, in part, on local knowledge bases associated with each of the plurality of edge devices, wherein the machine learning model is configured to learn representations [“With the application of federated learning, each device uses local data for local training, then uploads the model to the server for aggregation, and finally the server sends the model update to the participants to achieve the learning goal” Abstract; Fig. 1] of the vectorized dataset;
applying symbolic reasoning techniques to the symbolic representations to perform a reasoning task by passing the symbolic representations through data transformation nodes constituting the distributed computational graph [“multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggregator” Abstract; Fig. 1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Amazon and Zhang before him before the effective filing date of the claimed invention, to modify the system of Amazon to incorporate the federated learning of Zhang.
Given the advantage of data privacy and distributed computing for increased speed and efficiency, one having ordinary skill in the art would have been motivated to make this obvious modification.
However, Amazon fails to explicitly disclose mapping the learned representations to symbolic concepts or rules to create symbolic representations of the learned representations;
applying symbolic reasoning techniques to the symbolic representations to perform a reasoning task by passing the symbolic representations through data transformation nodes constituting the distributed computational graph;
engineering a prompt using an output of the reasoning task.
Futia discloses mapping the learned representations to symbolic concepts or rules to create symbolic representations of the learned representations [“predicted output of deep learning models can be mapped into entities of KGs [Knowledge Graphs] or concepts and relationships of ontologies (knowledge matching)” pg. 2 ¶2; Fig. 1];
applying symbolic reasoning techniques to the symbolic representations to perform a reasoning task by passing the symbolic representations [“KGs and ontologies are natively built to be queried and therefore they are able to provide answers to user requests (interactive explanations) and to provide a symbolic level to interpret the behaviour and the results of a deep learning model” pg. 2 ¶2; Fig. 1] through data transformation nodes constituting the distributed computational graph;
engineering a prompt using an output of the reasoning task [“provide answers” pg. 2 ¶2; Fig. 1].
It would have been obvious to one having ordinary skill in the art, having the teachings of Amazon, Zhang, and Futia before him before the effective filing date of the claimed invention, to modify the combination to incorporate the symbolic reasoning of Futia.
Given the advantage of explainable learning, one having ordinary skill in the art would have been motivated to make this obvious modification.
Regarding Claim 2, Amazon, Zhang, and Futia disclose the computing system of claim 1. Amazon further discloses wherein the plurality of context elements comprises retrieval-augmented generation (RAG) information [“Retrieval-Augmented Generation (RAG)” pg. 1].
Regarding claim 3, Amazon, Zhang, and Futia disclose the computing system of claim 2. Amazon further discloses wherein the RAG information is obtained from a RAG marketplace [“How can AWS support your Retrieval-Augmented Generation requirements? Amazon Bedrock is a fully-managed service” pg. 4].
Regarding Claim 4, Amazon, Zhang, and Futia disclose the computing system of claim 1. Amazon further discloses wherein the plurality of context elements comprises information associated with the user device [“RAG model augments the user input (or prompts) by adding the relevant retrieved data in context.” pg. 3 ¶5]
Regarding Claim 5, Amazon, Zhang, and Futia disclose the computing system of claim 1. Amazon further discloses wherein the plurality of context elements comprises information associated with an action a user of the user device is performing during interaction with the platform [“user query” pg. 3 ¶2; “If an employee searches, "How much annual leave do I have?"” pg. 3 ¶4].
Claims 8-12 are rejected on the same grounds as claims 1-5, respectively.
Claims 15-19 are rejected on the same grounds as claims 1-5, respectively.
Claims 22-26 are rejected on the same grounds as claims 1-5, respectively.
Claim(s) 6, 13, 20, 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amazon, Zhang, and Futia, in view of Lucas et al. (hereinafter Lucas), U.S. Patent Application Publication 2020/0176098.
Regarding Claim 6, Amazon, Zhang, and Futia disclose the computing system of claim 1.
However, Amazon fails to explicitly disclose wherein the expert knowledge comprises scored datasets or scored model output.
Lucas discloses wherein the expert knowledge comprises scored datasets or scored model output [“Some MLA may identify features of importance and identify a coefficient, or weight, to them. The coefficient may be multiplied with the occurrence frequency of the feature to generate a score, and once the scores of one or more features exceed a threshold, certain classifications may be predicted by the MLA.” ¶63; “individual words may be provided a weighting factor for probability of occurrence across a massive training set” ¶103].
It would have been obvious to one having ordinary skill in the art, having the teachings of Amazon, Zhang, Futia, and Lucas before him before the effective filing date of the claimed invention, to modify the combination to incorporate the feature weighting of Lucas.
Given the advantage of weighting features for a more accurate outcome, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim(s) 7, 14, 21, 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Amazon, Zhang, and Futia, and Lucas, in view of Hall, Knowledge Marketplaces: The New Learning Hubs For Business Leaders.
Regarding Claim 7, Amazon, Zhang, Futia, and Lucas disclose the computing system of claim 6.
However, Amazon fails to explicitly disclose wherein the expert knowledge is obtained from an expert knowledge marketplace.
Hall discloses wherein the expert knowledge is obtained from an expert knowledge marketplace [“a concept known as knowledge marketplaces, companies are building networks of market experts to help leaders in virtually every area of their business” pg. 2].
It would have been obvious to one having ordinary skill in the art, having the teachings of Amazon, Zhang, Futia, Lucas and Hall before him before the effective filing date of the claimed invention, to modify the combination to incorporate the knowledge marketplace of Hall.
Given the advantage of acquiring expertise and insights for more accurate results, one having ordinary skill in the art would have been motivated to make this obvious modification.
Claim 14 is rejected on the same grounds as claim 7.
Claim 21 is rejected on the same grounds as claim 7.
Claim 28 is rejected on the same grounds as claim 7.
Examiner’s Note
The Examiner respectfully requests of the Applicant in preparing responses, to fully consider the entirety of the reference(s) as potentially teaching all or part of the claimed invention. It is noted, REFERENCES ARE RELEVANT AS PRIOR ART FOR ALL THEY CONTAIN. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including non-preferred embodiments (see MPEP 2123). The Examiner has cited particular locations in the reference(s) as applied to the claim(s) above for the convenience of the Applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim(s), typically other passages and figures will apply as well.
Additionally, any claim amendments for any reason should include remarks indicating clear support in the originally filed specification.
Response to Arguments
Regarding the 101 rejection, Applicant's arguments have been fully considered but have been found unpersuasive. Applicant argues 1) the distributed computation graph provides a specific data structure that not an abstract idea but provides a practical application, and 2) the plurality of edge devices used in a federated learning system provides a specific technical benefit in data privacy and security, and 3) the combination of these two things provides a practical application. Examiner disagrees for at least the following reasons.
First, the distributed computation graph as claimed is not a specific data structure as the claim provides no specificity require for such a limited interpretation. Instead, it is interpreted as implementing distributed computing, which while an additional element, does not provide a practical application.
Second, the edge devices used in federated learning are additional elements. However, federated learning by definition has a plurality of edge devices and is a generic machine learning approach. While the edge devices are generic computer devices, the federated learning is merely applying the abstract idea in a computing environment.
Third, the combination of additional elements does not integrate the abstract idea into a practical application. The inclusion of generic computer components (i.e., the plurality of edge devices) and generic federated training of the learning model does not integrate the abstract idea into a practical application because the elements alone or in combination do not provide a meaningful limit on the judicial exception.
For at least these reasons, the rejections are maintained.
Regarding the 103 rejections, Applicant's arguments with respect to the claims have been considered but are moot because the arguments do not apply to the references being used in the current rejection of the limitations.
Conclusion
Any prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is reminded that in amending in response to a rejection of claims, the patentable novelty must be clearly shown in view of the state of the art disclosed by the references cited and the objections made. Applicant must also show how the amendments avoid such references and objections. See 37 CFR §1.111(c). Additionally when amending, in their remarks Applicant should particularly cite to the supporting paragraphs in the original disclosure for the amendments.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT H BEJCEK II whose telephone number is (571)270-3610. The examiner can normally be reached Monday - Friday: 9:00am - 5:00pm.
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/R.B./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148