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 .
Remarks
This action is in response to the amendments received on 9/10/26. Claims 1-15 and 17-21 are pending in the application. Claim 16 has been cancelled. Applicants' arguments have been carefully and respectfully considered.
Claims 1-15 and 17-21 are rejected under 35 U.S.C. 101.
Claims 1-15 and 17-21 are rejected under 35 U.S.C. 112.
Claims 1-15, 17, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. (US 2024/0419738), and further in view of Balduino et al. (US 2019/0362021) and Mangalam et al. (US 2025/0217603).
Claims 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma in view of Balduino and Mangalam, and further in view of Kang et al. (US 2025/0103944).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word
“means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35
U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder
that is coupled with functional language without reciting sufficient structure to perform
the recited function and the generic placeholder is not preceded by a structural modifier.
Such claim limitation(s) are: a segmentation module configured to segment a data
sample into ontological segments; a clustering module configured to cluster the
ontological segments; a subtree generation module configured to use at least one large
language model (LLM); and a taxonomy construction module configured to create the induced taxonomy, and a classification module configured to map the induced taxonomy in claim 1.
Because these claim limitation(s) are being interpreted under 35 U.S.C. 112(f) or
pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the
corresponding structure described in the specification as performing the claimed
function, and equivalents thereof.
If applicant does not intend to have these limitation(s) interpreted under 35
U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the
claim limitation(s) to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35
U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed
function); or (2) present a sufficient showing that the claim limitation(s) recite(s)
sufficient structure to perform the claimed function so as to avoid them being interpreted
under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-15 and 17-21 are rejected under 35 U.S.C. 112(b) or 35 U.S.C.
112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out
and distinctly claim the subject matter which the inventor or a joint inventor (or for
applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1 and 20 disclose “update the induced taxonomy in real-time” in the new amendment. The specification discloses “. The term “inference classification” refers to a process of assigning a category or label to the raw data based on the induced taxonomy. … Moreover, the inference classification allows the system to classify the raw data in real-time, supporting dynamic and continuous data streams.” Page 31 Li. 10-21. The specification does not support updating the induced taxonomy in real-time, but rather assigning a category or label to the raw data in real-time.
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 1-15 and 17-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Step 2A, Prong One asks: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? See MPEP 2106.04 Part I. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. See MPEP 2106.04(a).
With respect to claims 1 and 20, the limitation of “segment a data sample into ontological segments”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “segment” in the context of this claim encompasses the user mentally separating text to analyze. Similarly, the limitation of “cluster the ontological segments into ontological clusters”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, “clustering” in the context of this claim encompasses the user mentally grouping similar data. The limitation of “generate at least one subtree for each ontological cluster” and “create the induced taxonomy comprising a root node and a combination of the generated at least one subtree”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “generate” and “create” in the context of this claim encompasses the user mentally determining related data. The limitation of “map the induced taxonomy to a set of label configuration objects”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “map” in the context of this claim encompasses the user mentally determining related data. The limitation of “classify the raw data using the set of label configuration objects” and “inference classification of a raw data”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, nothing in the claim element precludes the step from practically being performed in the mind. For example, “classify” in the context of this claim encompasses the user mentally determining a type of data. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
At step 2a, prong two, this judicial exception is not integrated into a practical application. The claims recite a “processing arrangement”, however, this is recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Additionally, the claim recites “update the induced taxonomy.” These elements do not integrate the abstract idea into a practical application because they do not impose a meaningful limit on the judicial exception and provide only insignificant extra solution activity that is mere data gathering in conjunction with 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, the additional elements amount to no more than mere instructions to apply an exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept.
With respect to “update the induced taxonomy”, the courts have found limitations directed towards storing to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts") and “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Considering the additional elements individually and in combination and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. The claim is not patent eligible.
With respect to claims 2, 4, 5, 13, and 14, the limitations further define components previously addressed and do not provide significantly more than the judicial exception.
With respect to claim 3, the “speech-to-text converter” provides a conventional component without providing significantly more than the abstract idea.
With respect to claim 6-12, the limitations, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The limitations are directed towards mental processes of data analysis.
With respect to claim 15, the limitations are directed towards an “interface module,” the courts have found limitations directed towards outputting to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
With respect to claims 17-19, the limitations are directed towards language models which are recited as a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
With respect to claim 21, the limitations are recited as a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component.
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 1-15, 17, 20, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. (US 2024/0419738), and further in view of Balduino et al. (US 2019/0362021) and Mangalam et al. (US 2025/0217603).
With respect to claim 1, Sharma teaches a system comprising a processing arrangement, communicably coupled to the database arrangement (Sharma, pa 0016, The administrator computing device 12c, as well as any other computing device coupled to the computer network 10, may be used to input one or more documents into the document database), the processing arrangement configured to execute a set of modules comprising:
a clustering module configured to cluster the ontological segments into ontological clusters, wherein each ontological cluster is classified as a generic cluster or a specific cluster representing a singular concept using a pair-wise clustering evaluation to grouping pairs of datapoints of the ontological cluster based on similarity or distance from each other (Sharma, pa 0102, The topic clustering logic 48 performs cluster analysis on all of the topics generated by the document cluster categorization logic 46 … the topic clustering logic 48 may group topics into a cluster if their vectorizations are within a threshold similarity of each other (e.g., based on cosine similarity). Examiner note: cosine similarity is a pairwise measure);
a subtree generation module configured to use at least one large language model (LLM) (Sharma, pa 0103, for each cluster of topics identified by the document cluster categorization logic 46, the topic cluster categorization logic 50 may input the topics into a trained LLM) to generate at least one subtree for each ontological cluster, wherein each subtree has at least one of: a parent node and a leaf node, and wherein each node is indicative of concept data (Sharma, pa 0104, After the LLM receives the topics of a cluster and the prompt, the LLM may respond to the prompt by generating a name or category for the topics of the cluster. As such, each of the topics generated by the document cluster categorization logic 46 may be assigned to a named cluster. & pa 0161, the names of the topic clusters determined by the topic clustering logic 48 may be added as nodes below a leaf node of the taxonomy, and the topics within each of those clusters may be added as nodes below them.); and
a taxonomy construction module configured to create the induced taxonomy comprising a root node and a combination of the subtrees of the generated at least one subtree (Sharma, pa 0003, a highest level of a taxonomy may include a plurality of broad categories, each category of the first level may include a second level of more specific sub-categories, each sub-category of the second level may include a third level of even more specific sub-categories, and so on., pa 0004, As the document corpus changes, new topics may be formed, and other topics may fade. Accordingly, the hierarchical taxonomy should be revised over time to account for such changes. & pa 0161, the names of the topic clusters determined by the topic clustering logic 48 may be added as nodes below a leaf node of the taxonomy, and the topics within each of those clusters may be added as nodes below them. … the taxonomy expansion logic 52 may expand the taxonomy by two node levels.);
a classification module configured to map the induced taxonomy to a set of label configuration objects, wherein each label configuration object corresponds to a label in the induced taxonomy (Sharma, pa 0162, The documents associated with each topic, as determined by the document clustering logic 44 and the document cluster categorization logic 46, may be associated with the corresponding topic nodes.); and
classify the raw data using the set of label configuration objects derived from the mapped induced taxonomy via a zero-shot inference classification or fine-tuned inference pipeline (Sharma, pa 0165, the document cluster categorization logic 46 may input the documents associated with the cluster into an LLM, along with a prompt asking for one or more topics associated with the documents in the cluster. The LLM may output one or more topics in response to the prompt.).
Sharma doesn't expressly discuss a segmentation module configured to segment the data sample into ontological segments the database arrangement comprising of a plurality of data records and the data sample is selected as a subset of the data records.
Balduino teaches a segmentation module configured to segment a data sample from the database arrangement into ontological segments for creating an induced taxonomy (Balduino, identifying representative content 301, creating snippets/fragments (referred to herein after as snippets) from the representative content using optimal utterance segments ( e.g., dynamic windows) 302,);
a clustering module configured to cluster the ontological segments into ontological clusters, wherein each ontological cluster is classified as a generic cluster or a specific cluster representing a singular concept using a pair-wise clustering evaluation to grouping pairs of datapoints of the ontological cluster based on similarity or distance from each other (Balduino, pa 0055, clustering snippets using an unsupervised clustering model 303, where the clusters each correspond to one or more of topics or cluster labels from a set of identified and learned topics … merging coadjacent snippets in the content having the same dominant topic 304, annotating and labeling portions of the content 305 with human readable labels using the dominant topics of the snippets corresponding to the portions of the content); and
update the induced taxonomy in real-time based on an updated data sample from an updated database arrangement (Balduino, pa 0055, deploying the developed model 308 to receive new text input, applying the model in real-time to new snippets for salient topic identification during a conversation 309 (e.g., applying the methods of block 302 to newly developing text during an interaction to find the new snippets), and re-clustering on snippets with low scores across the deployed models to find new salient topics 310.);
the processing arrangement configured for inference classification of a raw data using the induced taxonomy (Balduino, pa 0055, deploying the developed model 308 to receive new text input, applying the model in real-time to new snippets for salient topic identification during a conversation 309);
a classification module configured to map the induced taxonomy to a set of label configuration objects, wherein each label configuration object corresponds to a label in the induced taxonomy (Balduino, pa 0055, clustering snippets using an unsupervised clustering model 303, where the clusters each correspond to one or more of topics or cluster labels from a set of identified and learned topics) and classify the raw data using the set of label configuration objects derived from the mapped induced taxonomy via a zero-shot inference classification or fine-tuned inference pipeline in real-time, supporting dynamic and continuous data streams (Balduino, pa 0055, deploying the developed model 308 to receive new text input, applying the model in real-time to new snippets for salient topic identification during a conversation 309).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Sharma with the teachings of Balduino because it allows systems to determine the intent of the user based on prior use (Balduino, pa 0028) and dynamic discovery of topics for reflecting the latest information (Balduino, pa 0056).
Sharma in view of Balduino doesn't expressly discuss the database arrangement comprising of a plurality of data records and the data sample is selected as a subset of the data records.
Mangalam teaches the database arrangement comprising of a plurality of data records and the data sample is selected as a subset of the data records (Mangalam, pa 0024, A data source 101 may store and provide content 103 having text that is used by an LLM to identify concepts that are clustered and used to train the neural network classifiers to categorize the concepts. The content 103 may include any content that includes natural language text. & pa 0089, The datastores (such as 101) may be a database, which may include, or interface to, for example, an Oracle™ relational database sold commercially by Oracle Corporation. Other databases, such as Informix™, DB2 or other data storage, including file-based, or query formats, platforms, or resources such as OLAP (On Line Analytical Processing), SQL (Structured Query Language), a SAN (storage area network), Microsoft Access™ or others may also be used, incorporated, or accessed. The database may comprise one or more such databases that reside in one or more physical devices and in one or more physical locations. The datastores may include cloud-based storage solutions. The database may store a plurality of types of data and/or files and associated data or file descriptions, administrative information, or any other data.).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified because it assists the LLM in identifying concepts to train the neural network classifiers (Mangalam, pa 0024).
With respect to claim 2, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, wherein the data sample is a set of at least one of: unstructured text-based data (Sharma, pa 0028), speech-based data.
With respect to claim 3, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, further comprising a speech-to-text converter to generate data for the system to process (Balduino, pa 0078, call text 1301 output by a speech-to-text application).
With respect to claim 4, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, wherein the at least one concept for a given domain includes at least one of: entity types, relationships between the entity types, roles of the entity types, and labels (Balduino, pa 0068, the labeling of the salient topics includes developing character rules and parsing rules to recognize patterns (such as telephone numbers, email addresses, person names)).
With respect to claim 5, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, wherein the concept data includes at least one of: a concept label, a phrase-type description of the concept; a definition of the concept, and a detailed description of the concept, a parent or leaf node label, a phrase-type description of the parent or leaf node label (Sharma, pa 0104, After the LLM receives the topics of a cluster and the prompt, the LLM may respond to the prompt by generating a name or category for the topics of the cluster.).
With respect to claim 6, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, wherein the clustering module is further configured to execute at least one of: to separate outlier data from the ontological clusters, based on a density-based clustering algorithm; to remove generic nodes from the ontological clusters; and to de-duplicate n-1 type nodes, from amongst the plurality of nodes, representing the singular concept while retaining an nth node representing the singular concept, optionally, wherein the nth node and the n-1 type nodes are leaf nodes (Balduino, pa 0055, re-clustering on snippets with low scores across the deployed models to find new salient topics 310.).
With respect to claim 7, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, wherein the clustering module is configured to use pair-wise clustering for the plurality of nodes and wherein the pair-wise clustering is based on a cosine similarity between each pair of the plurality of nodes (Sharma, pa 0102, the topic clustering logic 48 may group topics into a cluster if their vectorizations are within a threshold similarity of each other (e.g., based on cosine similarity).
With respect to claim 8, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, wherein the taxonomy construction module is configured to iterate over each subtree and to add it to the root node or a node of a previously added subtree in the taxonomy, and wherein when it is determined that a node of a given subtree is similar to a node in the taxonomy, the taxonomy construction module is configured to add the children of given node as children of the similar node in the taxonomy, and wherein when it is determined that no node of a given subtree is similar to a node in the taxonomy, the taxonomy construction module is configured to add the given subtree below the root node in the taxonomy (Sharma, pa 0104, After the LLM receives the topics of a cluster and the prompt, the LLM may respond to the prompt by generating a name or category for the topics of the cluster. As such, each of the topics generated by the document cluster categorization logic 46 may be assigned to a named cluster. Therefore, two additional node levels may be added to the taxonomy. The topic cluster names generated by the topic cluster categorization logic 50 may be added as additional nodes below a leaf node of the current taxonomy.).
With respect to claim 9, Sharma in view of Balduino and Mangalam teaches the system according to claim 8, wherein the taxonomy construction module is further configured to refine the taxonomy by performing : - an elimination of a node that is not a leaf node in a subtree, and wherein the eliminated node comprises a single child node (Balduino, pa 0055, re-clustering on snippets with low scores across the deployed models to find new salient topics 310. & pa 0088, processing to remove salient topics that are too frequent and neutral ( e.g., in terms of positive or negative outcome),).
With respect to claim 10, Sharma in view of Balduino and Mangalam teaches the system according to claim 9, wherein the taxonomy construction module is further configured to refine the taxonomy by replacing the eliminated node with a single child node (Balduino, pa 0055, re-clustering on snippets with low scores across the deployed models to find new salient topics 310.).
With respect to claim 11, Sharma in view of Balduino and Mangalam teaches the system according to claim 8 or claim 9, wherein the taxonomy construction module is further configured to assign scores to a given pair of nodes, each pair of nodes being selected from the plurality of nodes and the root node or the node of a previously added subtree in the taxonomy, and wherein based on the assigned scores, it is determined that the given pair of nodes is similar when the assigned score is higher than a predefined threshold (Balduino, pa 0060, merging coadjacent snippets having a distance less than a threshold 405.).
With respect to claim 12, Sharma in view of Balduino and Mangalam teaches the system according to claim 8 or claim 9, wherein the taxonomy construction module uses a cosine similarity between each pair of nodes (Balduino, pa 0061, a cosine distance between the vectors represents a similarity).
With respect to claim 13, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, wherein the set of modules use machine learning algorithms, and are trained using at least one of: unsupervised learning techniques, semi-supervised learning techniques, supervised learning techniques (Sharma, pa 0032).
With respect to claim 14, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, wherein the data sample is received from the database arrangement based on a user query received via a computing device associated with a user (Balduino, pa 0058, the representative content is data that includes interaction transcripts (301). Given the interaction transcripts, snippets of x utterances in length are generated using a sliding window).
With respect to claim 15, Sharma in view of Balduino and Mangalam teaches the system according to claim 14, further comprising an interface module configured to provide the induced taxonomy on the computing device associated with a user (Balduino, pa 0075, a user interface 900 (FIG. 9) is shown for creating an intent hierarchy 603, labeling and annotating the interaction snippets 604. In FIG. 9, refined intents 901 are displayed with their frequency and correlation).
With respect to claim 17, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, wherein the classification module is pre-trained on natural language inference (NLI) model to label the raw data according to the set of label configuration objects (Sharma, pa 0032-0034, LLMs are typically trained on a large set of natural language data, such that after they are trained, they may output responses to prompts. Examples of LLMS include GPT3, ChatGPT, and Gemini. In some examples, LLMs may also receive documents or other inputs along with a natural language prompt. For example, an LLM may be asked to analyze and provide a summary of a paper. [0033] In embodiments, the document cluster categorization logic 46 may submit a prompt to a trained LLM asking for a category or topic that categorizes the documents of a cluster. For example, a prompt of "what topic categorizes these documents?" may be submitted to an LLM along with the documents of a cluster. … In response to the input prompt, the LLM may generate one or more topics that categorize the documents of the cluster.).
With respect to claim 20, the limitations are essentially the same as claim 1, and are rejected for the same reasons.
With respect to claim 21, Sharma in view of Balduino and Mangalam teaches a non-transitory computer-readable storage media having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor comprising processing hardware to execute a method of claim 20 (Sharma, Fig. 2).
Claims 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma in view of Balduino and Mangalam, and further in view of Kang et al. (US 2025/0103944).
With respect to claim 18, Sharma in view of Balduino and Mangalam teaches the system according to claim 1, as discussed above.
Kang teaches wherein the processing arrangement further comprises a pre-trained transformer language model configured to perform a multi-label text classification of a first data according to the classified raw data, wherein the first data is received after classification of the raw data (Kang, pa 0022, the design system 100 instructs the prompt transformer (e.g., generative pre-trained transformer 4 (GPT-4)) to structure data from human experts 220 (e.g., AskNature posts, AskNature images, etc.) that is raw into problem-mechanism schemas. Here, the data can be a curated list of organisms with detailed descriptions facilitating unique strategies to functional problems (e.g., manage impact, modify speed, etc.). The organisms and strategies can be grouped by function and viewed as a list. To curate the seeds 210 towards quality mechanisms, the design system 100 may predict a functional problem p that is relevant to a domain (e.g., automobile designers) and exclude irrelevant functions such as adapt behaviors, adapt genotype, co-evolve, maintain community, etc. ).
It would have been obvious at the effective filing date of the invention to a person having ordinary skill in the art to which said subject matter pertains to have modified Sharma in view of Balduino and Mangalam with the teachings of Kang because it can provide expanded knowledge about data (Kang, pa 0017).
With respect to claim 19, Sharma in view of Balduino and Mangalam and Kang teaches the system according to claim 18, wherein the pre-trained transformer language model is configured to predict scores for each of the set of label configuration objects corresponding to the first data, simultaneously (Kang, pa 0022, the design system 100 instructs the prompt transformer (e.g., generative pre-trained transformer 4 (GPT-4)) to structure data from human experts 220 (e.g., AskNature posts, AskNature images, etc.) that is raw into problem-mechanism schemas. Here, the data can be a curated list of organisms with detailed descriptions facilitating unique strategies to functional problems (e.g., manage impact, modify speed, etc.). The organisms and strategies can be grouped by function and viewed as a list. To curate the seeds 210 towards quality mechanisms, the design system 100 may predict a functional problem p that is relevant to a domain (e.g., automobile designers) and exclude irrelevant functions such as adapt behaviors, adapt genotype, co-evolve, maintain community, etc. ).
Response to Arguments
35 U.S.C. 101
Applicant argues that the mental process exception is improperly applied as a judicial exception because the process of continuously updating the induced taxonomies based off of continuously updated data samples in real-time in a technical system environment is an architecture that cannot be performed within the human mind would require split second calculations for vast amounts of data. The Examiner respectfully disagrees. Real-time updating of data samples is not part of the judicial exception. The “update” limitation is an additional element that does not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and provides only insignificant extra solution activity. The courts have found limitations directed towards storing to be insignificant extra solution activity. See MPEP 2106.05(g). The limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). This is considered in Step 2A Prong Two and Step 2B. and v. Consulting and updating an activity log, Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754. Further, the amendment stating “classify the raw data … in real-time, supporting dynamic and continuous data streams” can be performed mentally under broadest reasonable interpretation because it does not require time or volume restrictions that prohibit mental analysis.
Applicant argues that the amendments integrate an abstract idea into a practical application because segmenting a data sample from a data arrangement provides benefits to the technical system. The Examiner respectfully disagrees. At Step 2A Prong Two, the recited abstract idea should be integrated into a practical application through recitation of additional elements. The limitation of “segment a data sample into ontological segments” under its broadest reasonable interpretation, covers performance of the limitation in the mind and is part of the abstract idea, not an additional element. The “update” limitation is an additional element that does not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and provides only insignificant extra solution activity. It merely updates the induced taxonomy without providing an improvement to the computer or technology. Individually and in combination, the additional element does not integrate the exception into a practical application.
Applicant argues that a smaller data sample in deriving an induced taxonomy is hugely beneficial and constantly updating the data sample multiple times to re-derive the induced taxonomy provides a cascading effect that provide “significantly more” under Prong Two, Step 2B of Alice. The Examiner respectfully disagrees. Prong Two step 2B asks, Does the claim recite additional elements that amount to significantly more than the judicial exception? Examiners should answer this question by first identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). As discussed above, the “segment” limitation is part of the abstract idea, not an additional element, and is not considered in Prong Two, Step 2B. With respect to the “update” amendment, limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include: … Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. See MPEP 2106.05 part A. The courts have found limitations directed towards storing to be well-understood, routine, and conventional. See MPEP 2106.05(d)(II). Electronic recordkeeping, Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014) (creating and maintaining "shadow accounts") and “storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
35 U.S.C. 103
Applicant argues that Sharma in view of Balduino and Mangalam doesn’t expressly discuss the new amendments because Balduino does not teach taking a subset of data from a database and constructing an induced taxonomy for future mapping with incoming raw data. The Examiner respectfully disagrees. Mangalam was provided to show taking the subset of data from a database because Balduino does not mention that the snippets are stored in a database, only that they are provided through interactions of the user (Balduino, pa 0056). However, Balduino does teach constructing an induced taxonomy for incoming raw data by creating the target taxonomy from the identified topics (Balduino, pa 0055).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huang et al. (US 2016/0364468) teaches clustering of records for creating a database index structure.
Deland et al. (US 2021/0311968) teaches modeling dynamically evolving data using dynamic clustering.
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.
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/BRITTANY N ALLEN/ Primary Examiner, Art Unit 2169
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