DETAILED ACTION
Introduction
This office action is in response to Applicant’s submission filed on July 31, 2024.
Claims 1-20 are pending in the application. As such, claims 1-20 have been examined.
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 .
Drawings
The drawings were received on July 31, 2024. These drawings have been accepted and considered by the Examiner.
Claim Objections
Claims 10-12 are objected to because of the following informalities:
Claim 10, line 7, reads “performe one of”. Examiner believes this to be a clerical error and it is intended to read “perform one of”. Claims 11 and 12 depend from claim 10 and therefore inherit this objection.
Appropriate correction is required.
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.
Claims 1, 9 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
A method, comprising:
receiving user data indicative of one or more attributes of one or more users and activity data indicating past actions by the one or more users in association with particular content items;
identifying, using a first machine learning model, a topic based on the activity data;
identifying, using a second machine learning model, a subset of attributes of the one or more attributes of the one or more users that are associated with the topic based on one or more of:
a score associated with the second machine learning model;
a weight associated with an attribute of the one or more attributes in the second machine learning model; or
a label associated with the topic;
generating a prompt based on the topic and the subset of attributes associated with the topic; and
generating, based on the prompt using a large language model (LLM), content to provide to a user having the subset of attributes associated with the topic.
The claim limitations, under their broadest reasonable interpretation, cover performance of the limitations in the mind. For example,
“receiving user data indicative of one or more attributes of one or more users and activity data indicating past actions by the one or more users in association with particular content items” in the context of this claim encompasses a person observing a user’s activity and collecting the data,
“identifying, using a first machine learning model, a topic based on the activity data” in the context of this claim encompasses a person determining the topic,
“identifying, using a second machine learning model, a subset of attributes of the one or more attributes of the one or more users that are associated with the topic based on one or more of: a score associated with the second machine learning model; a weight associated with an attribute of the one or more attributes in the second machine learning model; or a label associated with the topic” in the context of this claim encompasses a person determining the attributes and assigning a score, weight, or label,
“generating a prompt based on the topic and the subset of attributes associated with the topic” in the context of this claim encompasses a person creating a prompt for an LLM,
“generating, based on the prompt using a large language model (LLM), content to provide to a user having the subset of attributes associated with the topic” in the context of this claim encompasses a person feeding the prompt to the LLM and obtaining a result.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a first machine learning model
second machine learning model
a large language model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
The dependent claims do not add limitations that would either integrate the recited abstract idea into a practical application or could help the Claim as a whole to amount to significantly more than the Abstract idea identified for the Independent Claim.
Claims 2 and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
determining that an attribute in the one or more attributes of the one or more users does not have a predefined attribute designation;
converting, using a third machine learning model, the attribute in the one or more attributes into an encoding; and
performing one of:
assigning a predefined attribute designation to the attribute in the one or more attributes based on the encoding of the attribute meeting a threshold associated with an encoding of the predefined attribute designation; or
excluding the attribute from the one or more attributes based on the encoding of the attribute failing to meet the threshold associated with the encoding of the predefined attribute designation.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“determining that an attribute in the one or more attributes of the one or more users does not have a predefined attribute designation” in the context of this claim encompasses a person identifying that an attribute is new,
“converting, using a third machine learning model, the attribute in the one or more attributes into an encoding” in the context of this claim encompasses a person generating a vector for the attribute,
“assigning a predefined attribute designation to the attribute in the one or more attributes based on the encoding of the attribute meeting a threshold associated with an encoding of the predefined attribute designation” in the context of this claim encompasses a person classifying the attribute of it meets a threshold,
“excluding the attribute from the one or more attributes based on the encoding of the attribute failing to meet the threshold associated with the encoding of the predefined attribute designation” in the context of this claim encompasses a person going ahead and not classifying the attribute of it does not meet a threshold.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a third machine learning model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 3 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein the converting, using the third machine learning model, comprises
using one or more of a sentence transformer, a transformer, or a Bidirectional Encoder Representations from Transformers (BERT) encoder.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“using one or more of a sentence transformer, a transformer, or a Bidirectional Encoder Representations from Transformers (BERT) encoder” in the context of this claim encompasses a person using one of the specified generic models.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites no additional elements.
Accordingly, these no additional elements do not integrate the abstract idea into a practical application because they do 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 no additional elements do not provide an inventive concept. The claim is not patent eligible.
Claims 4 and 12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
further comprising
determining the threshold associated with the predefined attribute designation based on a cosine similarity with respect to the encoding of the predefined attribute designation.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“determining the threshold associated with the predefined attribute designation based on a cosine similarity with respect to the encoding of the predefined attribute designation” in the context of this claim encompasses a person using a cosine similarity calculation to determine threshold evaluation.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites no additional elements.
Accordingly, these no additional elements do not integrate the abstract idea into a practical application because they do 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 no additional elements do not provide an inventive concept. The claim is not patent eligible.
Claims 5, 13 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein the identifying, using the first machine learning model, comprises
using a BERTopic model.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“using a BERTopic model” in the context of this claim encompasses a person ensuring that a BERTopic model is used.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a first machine learning model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 6, 14 and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein the identifying, using the second machine learning model, comprises
using one or more of a decision tree, a random forest, a boosted tree, a linear regression, a logistic regression, a support vector machine, or a neural network.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“using one or more of a decision tree, a random forest, a boosted tree, a linear regression, a logistic regression, a support vector machine, or a neural network” in the context of this claim encompasses a person ensuring to use at least one of the processes delineated.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a second machine learning model
a support vector machine
a neural network.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 7, 15 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein the score is generated based on one or more of an accuracy or a mean average precision (mAP) of the second machine learning model.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the score is generated based on one or more of an accuracy or a mean average precision (mAP) of the second machine learning model” in the context of this claim encompasses a person ensuring to determine a score using at least one of the techniques listed.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites these additional elements. These additional elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea.
a second machine learning model.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do 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 elements are generic computer components and the hardware is generic computer components that are merely being used as a tool to perform the abstract idea that do not provide an inventive concept. The claim is not patent eligible.
Claims 8 and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
wherein the label associated with the topic includes information related to one or more of a click through rate associated with the topic, an amount of total purchases under the topic, or a mean amount of purchases under the topic within a given time window.
The additional limitations of the claim do not preclude the method from practically being performed in the mind. For example,
“wherein the label associated with the topic includes information related to one or more of a click through rate associated with the topic, an amount of total purchases under the topic, or a mean amount of purchases under the topic within a given time window” in the context of this claim encompasses a person ensuring the label contains at least one of the information items listed.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites no additional elements.
Accordingly, these no additional elements do not integrate the abstract idea into a practical application because they do 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 no additional elements do not provide an inventive concept. 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.
Claims 1, 6, 9, 14, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Miyassi et al. (US Patent Pub. No. 20220067816 A1), hereinafter Miyassi, in view of Manandise et al. (US Patent Pub. No. 20240127026 A1), hereinafter Manandise, in view of Mavrommatis et al. (US Patent Pub. No. 20230273982 A1), hereinafter Mavrommatis, in view of Finegan et al. (US Patent No. 11797780 B1), hereinafter Finegan.
Regarding claims 1, 9 and 17, Miyassi teaches a method, a system, and a non-transitory computer readable medium (Miyassi in [0013] teaches a method and a system, and in [claim 13] teaches a non-transitory computer-readable storage device),
comprising:
[claim 9 only] a memory including computer executable instructions (Miyassi in [0031] teaches using a computer with memory and processors which execute program instructions);
and
[claim 9 only] a processor configured to execute the computer executable instructions and cause the system to (Miyassi in [0031] teaches using a computer with memory and processors which execute program instructions):
[claim 17 only] comprising instructions to be executed in a computer system, wherein the instructions when executed in the computer system cause the computer system to (Miyassi in [0031] teaches using a computer with memory and processors which execute program instructions):
receiving user data indicative of one or more attributes of one or more users and activity data indicating past actions by the one or more users in association with particular content items (Miyassi in [0048] teaches using user data which has attributes, and in [0017] teaches using “clickstream data” which is defined as an electronic record of a user's activity collected over time, and in [0045] teaches historical data may include customer's profile, including their billing history, platform subscriptions, feature information, content purchases, client device characteristics, clickstream data recovered at a seasonal interval (e.g., a user's interaction with the service platform at a previous year's tax return window), and the like).
Miyassi does not teach, however Manandise teaches
identifying, using a first machine learning model, a topic based on the activity data (Manandise in [0057] teaches using a topic classifier which identifies the topic, and in [0005] teaches using a deep classifier including deep natural language machine learning model).
Manandise is considered to be analogous to the claimed invention because it is in the same field of MLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi further in view of Manandise to allow for using a topic classifier. Motivation to do so would allow for discriminating between queries (word embeddings) that look closely-related on the surface but have different meanings (Manandise [0117]).
Miyassi, as modified above, teaches the one or more attributes, the one or more users, and the topic.
Miyassi, as modified above, does not teach, however Mavrommatis teaches
identifying, using a second machine learning model, a subset of attributes [of the one or more attributes] of [the one or more users] that are associated with [the topic] (Mavrommatis in [0031] teaches ranking of subsets of attributes and selecting a subset of attributes, and in [0033] teaches using machine learning models)
based on one or more of:
a score associated with the second machine learning model;
a weight associated with an attribute of the one or more attributes in the second machine learning model;
or
a label associated with the topic (Mavrommatis in [0031] teaches determining a correlation between attributes and classes (topics), and maintaining a list of attribute labels based on the correlation and a correlation filter to filter attributes from login event information and generate login filtered attributes).
Mavrommatis is considered to be analogous to the claimed invention because it is in the same field of MLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Mavrommatis to allow for selecting a subset of attributes. Motivation to do so would allow for a predicted login class to be transmitted to a target application access control interface to allow or deny access, to the target application to determine whether to perform a particular operation, or to a separate component (Mavrommatis [0047]).
Miyassi, as modified above, teaches the subset of attributes, and the topic.
Miyassi, as modified above, does not teach, however Finegan teaches
generating a prompt [based on the topic and the subset of attributes associated with the topic] (Finegan in [col 1 lines 20-35] teaches includes generating a prompt from a set of keywords, using large language machine learning models, and generating an output from the prompt by a machine learning model);
and
generating, based on the prompt using a large language model (LLM), content to provide to a user [having the subset of attributes associated with the topic] (Finegan in [col 1 lines 20-35] teaches includes generating a prompt from a set of keywords, using large language machine learning models, and generating an output from the prompt by a large language machine learning model).
Finegan is considered to be analogous to the claimed invention because it is in the same field of LLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Finegan to allow for generating a prompt. Motivation to do so would allow for a user to select specific images according to personal preferences and their desired motif for a video clip (Finegan [col 9 lines 20-30]).
Regarding claims 6, 14 and 19, Miyassi, as modified above, teaches the method, and system, and non-transitory computer readable medium of claims 1, 9 and 17.
Miyassi, as modified above, does not teach, however Mavrommatis teaches
wherein the identifying, using the second machine learning model, comprises using one or more of
a decision tree (Mavrommatis in [0073] teaches using a hierarchical/tree structure),
a random forest,
a boosted tree,
a linear regression,
a logistic regression,
a support vector machine,
or
a neural network (Mavrommatis in [0102] teaches using a neural network).
Mavrommatis is considered to be analogous to the claimed invention because it is in the same field of MLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Mavrommatis to allow for using a neural network. Motivation to do so would allow for a predicted login class to be transmitted to a target application access control interface to allow or deny access, to the target application to determine whether to perform a particular operation, or to a separate component (Mavrommatis [0047]).
Claims 2 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Miyassi, in view of Manandise, in view of Mavrommatis, in view of Finegan, in view of Lesner et al. (US Patent No. 11657447 B1), hereinafter Lesner.
Regarding claims 2 and 10, Miyassi, as modified above, teaches the method, and system of claims 1 and 9.
Miyassi, as modified above, does not teach, however Mavrommatis teaches
further comprising:
[claim 10 only] wherein the processor is further configured to execute the computer executable instructions and cause the system to :
converting, using a third machine learning model, the attribute in the one or more attributes into an encoding (Mavrommatis in [0015] teaches using a machine learning model, and generating an encoding).
Mavrommatis is considered to be analogous to the claimed invention because it is in the same field of MLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Mavrommatis to allow for generating an encoding. Motivation to do so would allow for a predicted login class to be transmitted to a target application access control interface to allow or deny access, to the target application to determine whether to perform a particular operation, or to a separate component (Mavrommatis [0047]).
Miyassi, as modified above, teaches the encoding.
Miyassi, as modified above, does not teach, however Lesner teaches
determining that an attribute in the one or more attributes of the one or more users does not have a predefined attribute designation (Lesner in [col 9 lines 20-30] teaches inferring attributes such as the user's length of employment, start date, end date, income (e.g., total income or income over a period), and/or pay period (e.g., weekly, biweekly, monthly, inconsistent, etc.) for a given income source);
and
performing one of:
assigning a predefined attribute designation to the attribute in the one or more attributes based on [the encoding of] the attribute meeting a threshold associated with [an encoding] of the predefined attribute designation (Lesner in [col 9 lines 20-30] teaches selecting attributes, when thresholds are met by the attributes);
or
excluding the attribute from the one or more attributes based on [the encoding] of the attribute failing to meet the threshold associated with [the encoding] of the predefined attribute designation (Lesner in [col 9 lines 20-30] teaches not selecting attributes, when thresholds are not met by the attributes [not meeting the threshold would be followed by not selecting (excluding) the attribute]).
Lesner is considered to be analogous to the claimed invention because it is in the same field of analysis of user attributes. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Lesner to allow for evaluating if a threshold is met. Motivation to do so would allow for a processing apparatus to generate clusters along certain boundaries (Lesner [col 6 lines 43-64]).
Claims 3, 4, 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Miyassi, in view of Manandise, in view of Mavrommatis, in view of Finegan, in view of Lesner, in view of Qu et al. (US Patent Pub. No. 20230030870 A1), hereinafter Qu.
Regarding claims 3 and 11, Miyassi, as modified above, teaches the method, and system of claims 2 and 10.
Miyassi, as modified above, does not teach, however Qu teaches
wherein the converting, using the third machine learning model, comprises using one or more of
a sentence transformer (Qu in [0044] teaches using a sentence transformer),
a transformer (Qu in [0044] teaches using a transformer),
or
a Bidirectional Encoder Representations from Transformers (BERT) encoder (Qu in [0044] teaches using a BERT transformer).
Qu is considered to be analogous to the claimed invention because it is in the same field of MLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Qu to allow for using a transformer. Motivation to do so would allow for providing an indication to a user of an agent transcript sentence containing utterances that are similar to utterances identified to be related to an intent to poach a customer (Qu [0018]).
Regarding claims 4 and 12, Miyassi, as modified above, teaches the method, and system of claims 2 and 10.
Miyassi, as modified above, does not teach, however Qu teaches
[claim 12 only] wherein the processor is further configured to execute the computer executable instructions and cause the system to:
further comprising determining the threshold associated with the predefined attribute designation based on a cosine similarity with respect to the encoding of the predefined attribute designation (Qu in [0044] teaches determining the semantic similarity score comprises determining a cosine similarity between the at least one corpus sentence embedding and the at least one query sentence embedding).
Qu is considered to be analogous to the claimed invention because it is in the same field of MLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Qu to allow for determining the semantic similarity score using a cosine similarity. Motivation to do so would allow for providing an indication to a user of an agent transcript sentence containing utterances that are similar to utterances identified to be related to an intent to poach a customer (Qu [0018]).
Claims 5, 13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Miyassi, in view of Manandise, in view of Mavrommatis, in view of Finegan, in view of Pita et al. (US Patent Pub. No. 20240152841 A1), hereinafter Pita.
Regarding claims 5, 13 and 18, Miyassi, as modified above, teaches the method, and system, and non-transitory computer readable medium of claims 1, 9 and 17.
Miyassi, as modified above, does not teach, however Pita teaches
wherein the identifying, using the first machine learning model, comprises using a BERTopic model (Pita in [0102] teaches using a pre-trained topic model, such as a Bidirectional Encoder Representations from Transformers (BERT) topic model (e.g., BERTopic model), to extract out topics that may then be used as features to machine learning model).
Pita is considered to be analogous to the claimed invention because it is in the same field of MLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Pita to allow for using a BERTopic model. Motivation to do so would allow for predicting budget codes for data objects representing construction-project-related action items and then use those budget-code predictions as a basis for providing construction-related insights, and the provided insights may be independent of any particular ongoing construction project and/or related to an ongoing construction project, then the computing platform may predict budget codes for data objects representing construction-project-related action items and provide construction-related insights in various ways (Pita [0009]).
Claims 7, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Miyassi, in view of Manandise, in view of Mavrommatis, in view of Finegan, in view of Betley et al. (US Patent Pub. No. 20180082703 A1), hereinafter Betley.
Regarding claims 7, 15 and 20, Miyassi, as modified above, teaches the method, and system, and non-transitory computer readable medium of claims 1, 9 and 17.
Miyassi, as modified above, does not teach, however Betley teaches
wherein the score is generated based on one or more of
an accuracy (Betley in [0032] teaches outputting a score based on attributes)
or
a mean average precision (mAP) of the second machine learning model.
Betley is considered to be analogous to the claimed invention because it is in the same field of determining accuracy scores. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Betley to allow for using a score based on attributes. Motivation to do so would allow for a score for content, which correlates to its suitability for machine speech recognition (Betley [0010]).
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Miyassi, in view of Manandise, in view of Mavrommatis, in view of Finegan, in view of Morin et al. (US Patent Pub. No. 20210342927 A1), hereinafter Morin.
Regarding claims 8 and 16, Miyassi, as modified above, teaches the method, and non-transitory computer readable medium of claims 1 and 17.
Miyassi, as modified above, does not teach, however Mavrommatis teaches
wherein the label associated with the topic includes information (Mavrommatis in [0031] teaches using attribute labels).
Mavrommatis is considered to be analogous to the claimed invention because it is in the same field of MLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Mavrommatis to allow for using a neural network. Motivation to do so would allow for a predicted login class to be transmitted to a target application access control interface to allow or deny access, to the target application to determine whether to perform a particular operation, or to a separate component (Mavrommatis [0047]).
Miyassi, as modified above, does not teach, however Morin teaches
related to one or more of
a click through rate associated with the topic (Morin in [0037] teaches using a click-through-rate for a topic),
an amount of total purchases under the topic,
or
a mean amount of purchases under the topic within a given time window.
Morin is considered to be analogous to the claimed invention because it is in the same field of MLMs. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Miyassi, as modified above, further in view of Morin to allow for using a click-through-rate for a topic. Motivation to do so would allow for a user of the online system or other entity presented with the graphs to view graphs corresponding to the values of the performance metrics for different periods of time, for different geographic locations, etc. (Morin [0044]).
Conclusion
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PAUL MUELLER
Examiner
Art Unit 2657
/PAUL J. MUELLER/Examiner, Art Unit 2657