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 .
Response to Amendment
This Office Action is in response to the amendment filed on 05/06/26.
The applicant’s remarks and amendments to the claims were considered and results as
follow: THIS ACTION IS MADE FINAL.
Claims 1-6, 8, 10-14,16 and 18-19 have been amended. No claims have been
cancelled. No claims have been added. As a result, claims 1-20 are now pending in this office action.
Applicant’s amendment to claims 1-20 with respect to the rejection of claims under 35 U.S.C. 101 has been fully considered. However, the rejection has been maintained.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine,
manufacture, or composition of matter, or any new and useful improvement
thereof, may obtain a patent therefor, subject to the conditions and requirements
of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea without significantly more.
Claim 1 recites, “receiving a hierarchical document tree including parent nodes and paragraph nodes each parent node and paragraph node associated with a portion of a source document; generating, using a machine learning model and based at least on a query prompt and contents of each of the parent and paragraph nodes, a similarity score for each of the parent and paragraph nodes; wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node without regard to the content of any other node in the hierarchical document tree; calculating combined similarity scores associated with each of one or more of the paragraph nodes based at least on the similarity score associated with the paragraph node and similarity scores associated with at least parent nodes having a parent relationship to the paragraph node; and generating a query result based at least on the combined similarity scores associated with the paragraph nodes”.
The limitation of “receiving a hierarchical document tree including parent nodes and paragraph nodes each parent node and paragraph node associated with a portion of a source document; generating, using a machine learning model and based at least on a query prompt and contents of each of the parent and paragraph nodes, a similarity score for each of the parent and paragraph nodes; wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node without regard to the content of any other node in the hierarchical document tree; calculating combined similarity scores associated with each of one or more of the paragraph nodes based at least on the similarity score associated with the paragraph node and similarity scores associated with at least parent nodes having a parent relationship to the paragraph node; and generating a query result based at least on the combined similarity scores associated with the paragraph nodes”. Nothing in the claim element precludes the step from practically being mental process and mathematical concept. A combination of judicial exceptions is still a recitation of an abstract idea (MPEP 2106.04(II)(B)). Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Claim
2 recites wherein each of the parent nodes is associated with a heading or subheading included in the source document in claim 2. But one or more parent nodes is associated with a heading or subheading included in the source document does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Claim
3 recites wherein each of the one or more paragraph nodes is associated with a paragraph, figure, image, drawing, or table included in the source document in claim 3. But one or more child nodes is associated a paragraph included in the source document does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Claim
4 recites wherein the combined similarity score is based at least on a weighted combination of the similarity score associated with the paragraph node and the similarity scores associated with the at least two parent nodes having a parent relationship to the paragraph node in claim 4. But the combined similarity score is based at least on a weighted combination of the similarity score associated with the child node and the one or more similarity scores associated with the one or more nodes having a parent relationship to the child node does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Claim
5 recites wherein the query result includes all or a subset of the contents of a paragraph node included in the in the hierarchical document in claim 5. But the query result includes the contents of a paragraph included in the source document does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Claim
6 recites generating one or more vector embeddings associated with each of the parent nodes and generating one or more vector embeddings associated with each of the paragraph nodes in claim 6. But generating one or more vector embeddings associated with each of the one or more parent nodes and generating one or more vector embeddings associated with each of the one or more child nodes does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Claim
7 recites generating one or more vector embeddings associated with one or more search terms included in the query prompt in claim 7. But generating one or more vector embeddings associated with one or more search terms does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 8 is dependent on claim 1 and includes all the limitations of claim 1. Claim
8 recites wherein the similarity score associated with a parent node or a child node is based at least on a comparison between first vector embeddings associated with the contents of the parent node or child node and second vector embeddings associated with one or more search terms included in the query prompt in claim 8. But the similarity score associated with a parent node or a child node is based at least on a comparison between first vector embeddings associated with the contents of the parent node or paragraph node and second vector embeddings associated with one or more search terms included in the query does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 9 is dependent on claim 1 and includes all the limitations of claim 1. Claim
9 recites a control system for an autonomous or semi-autonomous machine; a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations; a system for performing digital twin operations; a system for performing light transport simulation; a system for performing collaborative content creation for 3D assets in claim 9. But a perception system for an autonomous or semi-autonomous machine; a system for performing simulation operations does not go beyond the abstract idea itself. There are no additional components in the claim that would make it significantly more than the abstract idea.
Claim 10 recites the same limitations as claim 1 above. Therefore, claim
is rejected based on the same reasoning.
Claim 11 recites the same limitations as claim 2 above. Therefore, claim
s rejected based on the same reasoning.
Claim 12 recites the same limitations as claim 3 above. Therefore, claim
s rejected based on the same reasoning.
Claim 13 recites the same limitations as claim 4 above. Therefore, claim
is rejected based on the same reasoning.
Claim 14 recites the same limitations as claim 6 above. Therefore, claim
is rejected based on the same reasoning.
Claim 15 recites the same limitations as claim 7 above. Therefore, claim
is rejected based on the same reasoning.
Claim 16 recites the same limitations as claim 8 above. Therefore, claim
is rejected based on the same reasoning.
Claim 17 recites the same limitations as claim 9 above. Therefore, claim 17 is rejected based on the same reasoning.
Claim 18 recites, “generating, using a machine learning model and based at least on a query prompt and contents of each of the one or more parent and child paragraph nodes, a similarity score for each of the parent and paragraph nodes, wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node without regard to the content of any other node in the hierarchical document tree; calculating combined similarity scores associated with each of one or more of the paragraph nodes based at least on the similarity score associated with the paragraph node and one-or-more-similarity scores associated with at least two parent nodes having a parent relationship to the paragraph node; and generating a query result based at least on the combined similarity scores associated with the paragraph nodes.”.
The limitation of “generating, using a machine learning model and based at least on a query prompt and contents of each of the one or more parent and child paragraph nodes, a similarity score for each of the parent and paragraph nodes, wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node without regard to the content of any other node in the hierarchical document tree; calculating combined similarity scores associated with each of one or more of the paragraph nodes based at least on the similarity score associated with the paragraph node and one-or-more-similarity scores associated with at least two parent nodes having a parent relationship to the paragraph node; and generating a query result based at least on the combined similarity scores associated with the paragraph nodes”. That is, other than reciting, “processor”, nothing in the claim element precludes the step from practically being mental process and mathematical concept. Accordingly, the claim recites an abstract idea. This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform, receiving, generating, calculating and generating steps. The processor in each steps is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. A combination of judicial exceptions is still a recitation of an abstract idea (MPEP 2106.04(II)(B)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea. The claim does 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, the additional element of using a processor to perform, receiving, generating, calculating and generating steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 19 recites the same limitations as claim 4 above. Therefore, claim
19 is rejected based on the same reasoning.
Claim 20 recites the same limitations as claim 9 above. Therefore, claim 20 is rejected based on the same reasoning.
Claim Rejections 35 U.S.C. §103
5. 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 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.
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.
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:
Claims 1, 4, 6-7, 10, 13-15 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over SCHNEUWLY et al. (US 2022/0197917 A1) in view of Wheeler et al. (US Patent No. 6, 738, 759 B1).
Regarding claim 1 SCHNEUWLY teaches a computer-implemented method comprising:
receiving a hierarchical document tree, (See SCHNEUWLY paragraph [0024], ach of trees 111-114 instead represents a hierarchical document), including parent nodes and paragraph nodes each parent node and paragraph node associated with a portion of a source document, (See SCHNEUWLY paragraph [0083], A parent node may be connected to a child node by a referential link);
generating, using a machine learning model and based at least on a query prompt, (See SCHNEUWLY paragraph [0016], machine learning (ML)…database queries and many normal database queries) and contents of each of the parent and paragraph nodes, (See SCHNEUWLY paragraph [0039], A parent node without branching has a degree of one, which means that the parent node directly has only one child node), a similarity score for each of the parent and paragraph nodes, (See SCHNEUWLY paragraph [0081], a multiplicative product of similarity scores of comparisons of respective direct child nodes of two parent nodes);
calculating combined similarity scores associated with each of one or more of the paragraph nodes based at least on the similarity score associated with the paragraph node, (See SCHNEUWLY paragraph [0021], a respective tree similarity score is calculated. An ML model inferences based on the tree similarity scores of the many trees), and similarity scores associated with at least two parent nodes having a parent relationship to the paragraph node, (See SCHNEUWLY paragraph [0021], a multiplicative product of similarity scores of comparisons of respective direct child nodes of two parent nodes. In an embodiment, mathematics for calculating a pairwise comparison of children of two nodes as a scalar number are based on the following multiplication formula).
SCHNEUWLY does not explicitly disclose wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node without regard to the content of any other node in the hierarchical document tree and generating a query result based at least on the combined similarity scores associated with the paragraph nodes.
However, Wheeler teaches wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node, (See Wheeler; Col. 4 lines 16-21, computing a parent score for the current parent node using the data item scores of its children and a parent score computing algorithm and saving the parent node score. If the current parent node is a root node, saving the parent node score as the similarity search score and processing ends); without regard to the content of any other node in the hierarchical document tree, (See Wheeler; Col. 2 lines 58-61, The schema defines a hierarchy of parent and child nodes within the set of hierarchical documents. A node label is assigned to each node in the schema), generating a query result based at least on the combined similarity scores associated with the one or more child nodes, (See Wheeler; Col. 11 lines 19-24, relating child data with parent data using relation bands 40, executing the query according to the algorithm chosen by the user, scoring the results from the query and combining the child scores into the parent scores according to an algorithm chosen by the user).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node without regard to the content of any other node in the hierarchical document tree and generating a query result based at least on the combined similarity scores associated with the paragraph nodes of Wheeler in order to provide information about the resources in a manner that is useful to the users.
Claim 10 recites the same limitations as claim 1 above. Therefore, claim
is rejected based on the same reasoning.
Regarding claim 4, SCHNEUWLY taught the computer-implemented method according to claim 1, as described above.
SCHNEUWLY does not explicitly disclose wherein the combined similarity score is based at least on a weighted combination of the similarity score associated with the paragraph node, and the similarity scores associated with the at least two parent nodes having a parent relationship to the paragraph node.
However, Wheeler teaches wherein the combined similarity score is based at least on a weighted combination of the similarity score associated with the paragraph node, (See Wheeler Col. 11 lines 19-24, relating child data with parent data using relation bands 40, executing the query according to the algorithm chosen by the user, scoring the results from the query and combining the child scores into the parent scores according to an algorithm chosen by the user), and the similarity scores associated with the at least two parent nodes having a parent relationship to the paragraph node, (See Wheeler Col. 4 lines 43-47, the parent node score is saved as a final similarity score and processing ends. Otherwise, beginning with a lowest level of interior nodes in a schema, processing comprises for each interior node: saving the current parent node score as an interior node score, setting the current parent node to the parent of the interior node).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein the combined similarity score is based at least on a weighted combination of the similarity score associated with the paragraph node, and the similarity scores associated with the at least two parent nodes having a parent relationship to the paragraph node of Wheeler in order to provide information about the resources in a manner that is useful to the users.
Claims 13 and 19 recite the same limitations as claim 4 above. Therefore, claims
and 19 are rejected based on the same reasoning.
Regarding claim 6, SCHNEUWLY taught the computer-implemented method according to claim 1, as described above. SCHNEUWLY further teaches further comprising generating one or more vector embeddings associated with each of the parent nodes, (See SCHNEUWLY paragraph [0005], a feature vector be wide enough to accommodate a biggest expected tree such as a tallest tree, a widest tree, and/or a tree that contains the most tree nodes). and generating vector embeddings associated with each of the paragraph nodes, (See SCHNEUWLY paragraph [0005], a feature vector be wide enough to accommodate a biggest expected tree such as a tallest tree, a widest tree, and/or a tree that contains the most tree nodes).
Claim 14 recites the same limitations as claim 6 above. Therefore, claim
14 is rejected based on the same reasoning.
Regarding claim 7, SCHNEUWLY taught the computer-implemented method according to claim 1, as described above. Glass SCHNEUWLY further teaches further comprising generating one or more vector embeddings associated with one or more search terms included in the query prompt, (See SCHNEUWLY paragraph [0016], a feature vector for inferencing by an ML model, herein are acceleration techniques for measured comparison of parse trees such as for one or a few suspect database queries and many normal database queries).
Claim 15 recites the same limitations as claim 7 above. Therefore, claim
15 is rejected based on the same reasoning.
Regarding claim 18, SCHNEUWLY a system comprising: one or more processors to execute operations comprising:
generating, using a machine learning model, (See SCHNEUWLY paragraph [0016], machine learning (ML)…database queries and many normal database queries), and based at least on a query prompt and contents of each of the parent and paragraph, (See SCHNEUWLY paragraph [0039], the parent node directly has only one child node, although that child node may directly or indirectly have other child node(s)), a similarity score for each of the parent and paragraph nodes, (See SCHNEUWLY paragraph [0081], a multiplicative product of similarity scores of comparisons of respective direct child nodes of two parent nodes);
calculating combined similarity scores associated with each of one or more of the paragraph nodes, (See SCHNEUWLY paragraph [0021], a respective tree similarity score is calculated. An ML model inferences based on the tree similarity scores of the many trees), based at least on the similarity score associated with the paragraph node and similarity scores associated with at least two parent nodes having a parent relationship to the paragraph node, (See SCHNEUWLY paragraph [0021], a multiplicative product of similarity scores of comparisons of respective direct child nodes of two parent nodes. In an embodiment, mathematics for calculating a pairwise comparison of children of two nodes as a scalar number are based on the following multiplication formula).
SCHNEUWLY does not explicitly disclose wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node without regard to the content of any other node in the hierarchical document tree and generating a query result based at least on the combined similarity scores associated with the paragraph nodes.
However, Wheeler teaches wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node, (See Wheeler; Col. 4 lines 16-21, computing a parent score for the current parent node using the data item scores of its children and a parent score computing algorithm and saving the parent node score. If the current parent node is a root node, saving the parent node score as the similarity search score and processing ends); without regard to the content of any other node in the hierarchical document tree, (See Wheeler; Col. 2 lines 58-61, The schema defines a hierarchy of parent and child nodes within the set of hierarchical documents. A node label is assigned to each node in the schema), and generating a query result based at least on the combined similarity scores associated with the paragraph nodes, (See Wheeler; Col. 11 lines 19-24, relating child data with parent data using relation bands 40, executing the query according to the algorithm chosen by the user, scoring the results from the query and combining the child scores into the parent scores according to an algorithm chosen by the user).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein a similarity score associated with each parent node or each paragraph node is based at least on the content of the parent node or paragraph node without regard to the content of any other node in the hierarchical document tree and generating a query result based at least on the combined similarity scores associated with the paragraph nodes of Wheeler in order to provide information about the resources in a manner that is useful to the users.
Claims 3, 5, 8, 12 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over SCHNEUWLY et al. (US 2022/0197917 A1) in view of Wheeler et al. (US Patent No. 6, 738, 759 B1) and further in view of Quirk et al. (US 2018/0189269 A1)
Regarding claim 3, SCHNEUWLY together with Wheele taught the computer-implemented method according to claim 1, as described above.
SCHNEUWLY together with Wheele does not explicitly disclose wherein each of the one or more paragraph nodes is associated with a paragraph, figure, image, drawing, or table included included in the source document.
However, Quirk teaches wherein each of the one or more paragraph nodes is associated with a paragraph, figure, image, drawing, or table included included in the source document, (See Quirk paragraph [0070, a vocabulary section of the paper, will not be returned, whereas a paper that mentions the tuple, even across natural language segments (sentences, paragraphs, etc.) or not in close proximity (e.g., after a lengthy parenthetical aside), will be returned).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein each of the one or more paragraph nodes is associated with a paragraph, figure, image, drawing, or table included included in the source document of Quirk in order to finding exact words and relationships between those words to return results, and semantics that are expressed in various ways through natural language are lost in current document searches.
Claim 12 recites the same limitations as claim 3 above. Therefore, claim
s rejected based on the same reasoning.
Regarding claim 5, SCHNEUWLY taught the computer-implemented method according to claim 1, as described above.
SCHNEUWLY together with Wheele does not explicitly wherein the query result includes all or a subset of the contents of a paragraph node included in the hierarchical document tree source document.
However, Quirk teaches wherein the query result includes all or a subset of the contents of a paragraph node included in the hierarchical document tree source document, (See Quirk paragraph [0018], a file repository 110 contains one or more documents 120 that include natural language content. The natural language content may be divided into one or more segments, such as, words, sentences, paragraphs).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein the query result includes all or a subset of the contents of a paragraph node included in the hierarchical document tree source document of Quirk in order to finding exact words and relationships between those words to return results, and semantics that are expressed in various ways through natural language are lost in current document searches.
Regarding claim 8, SCHNEUWLY taught the computer-implemented method according to claim 1, as described above. SCHNEUWLY further teaches wherein the similarity score associated with a parent node or a paragraph node, (See SCHNEUWLY paragraph [0018], parent nodes having an amount of immediate child nodes), is based at least on a comparison between first vector embeddings associated with the contents of the parent node or paragraph node, (See SCHNEUWLY paragraph [0025], Tree similarity is numerically measured by counting matching subtrees when comparing trees in a pairing of two trees. For example, pairing 150 associates two trees 113-114. Tree similarity score 161 is the numerically measured similarity of the two trees of pairing 150).
SCHNEUWLY together with Wheele does not explicitly wherein the query result includes the contents of a paragraph included in the source document.
However, Quirk teaches second vector embeddings associated with one or more search terms included in the query prompt, (See Quirk paragraph [0046]The fields from the database are provided for search in response to queries, which may provide results that link to the documents 120 for which the relationships were discovered, a related field, or a derived answer. For example, a user may query a database with several key terms to return a related key term).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein the query result includes the contents of a paragraph included in the source document of Quirk in order to finding exact words and relationships between those words to return results, and semantics that are expressed in various ways through natural language are lost in current document searches.
Claim 16 recites the same limitations as claim 8 above. Therefore, claim
16 is rejected based on the same reasoning.
Claims 9, 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable
over SCHNEUWLY et al. (US 2022/0197917 A1) in view of Wheeler et al. (US Patent No. 6, 738, 759 B1) and further in view of Cella et al. (US 2025/0278066 A1).
Regarding claim 9, SCHNEUWLY taught the computer-implemented method according to claim 1, as described above. SCHNEUWLY further teaches wherein the method is performed by at least one of:
a system for performing deep learning operations, (See SCHNEUWLY paragraph [0155], network depth (i.e. amount of layers) may cause computational latency. Deep learning entails endowing a multilayer perceptron (MLP) with many layers);
a system for performing remote operations, (See SCHNEUWLY paragraph [0116],a remote computer);
a system for performing real-time streaming, (See SCHNEUWLY paragraph [0020], real time inferencing with live streaming logic);
a system for generating or presenting one or more of augmented reality content, virtual reality content, or mixed reality content, (See SCHNEUWLY paragraph [0023], a virtual computer, or other computing device);
a system implemented using an edge device, (See SCHNEUWLY paragraph [0139], a machine learning model that at a high level models a system of neurons interconnected by directed edges);
a system implementing one or more multi-model language models, (See SCHNEUWLY paragraph [0023], Computer 100 accelerates comparison of trees 111-114 such as for further analysis by machine learning (ML) model 170);
a system implementing one or more large language models (LLMs); (See SCHNEUWLY paragraph [0126], VMs), (VMM 730 instantiates and runs one or more virtual machine);
a system implementing one or more vision language models (VLMs); (See SCHNEUWLY paragraph [0126], VMs), (VMM 730 instantiates and runs one or more virtual machine);
a system for generating synthetic data, (See SCHNEUWLY paragraph [0062], A parent node is generated for each nonterminal symbol. Nonterminals are synthetic);
a system incorporating one or more virtual machines (See SCHNEUWLY paragraph [0126], VMs), (VMM 730 instantiates and runs one or more virtual machine);
a system implemented at least partially in a data center; or a system implemented at least partially using cloud computing resources, (See SCHNEUWLY paragraph [0130], The term “cloud computing” is generally used herein to describe a computing model which enables on-demand access to a shared pool of computing resources).
SCHNEUWLY together with Wheele does not explicitly disclose a control system for an autonomous or semi-autonomous machine, capabilities and solutions described herein, a perception system for an autonomous or semi-autonomous machine, , a system for performing simulation operations, a system for performing digital twin operations, a system for performing light transport simulation, a system for performing collaborative content creation for 3D assets, a system implemented using a robot, a system for performing conversational AI operations, a system for generating synthetic data using AI.
However, Cella teaches a control system for an autonomous or semi-autonomous machine, capabilities and solutions described herein, (See Cella paragraph [0029], capabilities and solutions described herein (such as optimization, autonomous operation, prediction, control, orchestration, or the like) should be understood to be capable of implementation by operation on a model or rule set); a perception system for an autonomous or semi-autonomous machine, , (See Cella paragraph [0029], autonomous operation, prediction, control, orchestration, or the like) should be understood to be capable of implementation by operation on a model or rule set); a system for performing simulation operations, (See Cella paragraph [0026], a set of adaptive energy digital twin systems 134, and/or a set of energy simulation systems 136); a system for performing digital twin operations, (See Cella paragraph [0026], a set of adaptive energy digital twin systems 134); a system for performing light transport simulation, (See Cella paragraph [0026], a set of adaptive energy digital twin systems 134, and/or a set of energy simulation systems 136); a system for performing collaborative content creation for 3D assets, (See Cella paragraph [0112], The simulation systems 136 may employ a wide range of simulation capabilities, such as 3D visualization simulation of behavior of physical); a system implemented using a robot, (See Cella paragraph [0029], supervised learning systems, robotic process automation systems); a system for performing conversational AI operations, (See Cella paragraph [0369], a communication co-processor, a video co-processor, and an artificial intelligence (AI) co-processor); a system for generating synthetic data using AI, (See Cella paragraph [0025], a set of AI-based energy orchestration, optimization, and automation systems 114 and a set of configurable data and intelligence modules and services 118).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify a control system for an autonomous or semi-autonomous machine, capabilities and solutions described herein, a perception system for an autonomous or semi-autonomous machine, , a system for performing simulation operations, a system for performing digital twin operations, a system for performing light transport simulation, a system for performing collaborative content creation for 3D assets, a system implemented using a robot, a system for performing conversational AI operations, a system for generating synthetic data using AI of Cella in order to facilitate management of energy in a more decentralized system, including edge and Internet of Things networking technologies, advanced computation and artificial intelligence technologies.
Claims 17 and 20 recites the same limitations as claim 9 above. Therefore, claims 17 and 20 are rejected based on the same reasoning.
Claims 2 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over SCHNEUWLY et al. (US 2022/0197917 A1) in view of Wheeler et al. (US Patent No. 6, 738, 759 B1) and further in view of Gupta et al. (US Patent No. 9, 959, 315 B1)
Regarding claim 2, SCHNEUWLY together with Wheeler taught the computer-implemented method according to claim 1, as described above.
SCHNEUWLY together with Wheeler does not explicitly disclose wherein each of the parent nodes is associated with a title heading, or subheading included in the source document.
However, Gupta teaches wherein each of the parent nodes is associated with a title heading, or subheading included in the source document, (See S Gupta Col. 8 lines 4-7 The heading hierarchy has two or more heading levels hierarchically arranged in parent-child relationships. The first level is the root heading, which, for example, is the title of the resource).
It would have been obvious to one with ordinary skill in the art before the
effective filing date of the claimed invention was made, to modify wherein each of the parent nodes is associated with a title heading, or subheading included in the source document of Gupta in order to provide information about the resources in a manner
that is useful to the users.
Claim 11 recites the same limitations as claim 2 above. Therefore, claim
s rejected based on the same reasoning.
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.
Conclusions/Points of Contacts
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 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 MULUEMEBET GURMU whose telephone number is (571)270-7095. The examiner can normally be reached M-F 9am - 5pm.
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/MULUEMEBET GURMU/Primary Examiner, Art Unit 2163
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