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 .
In response to Applicant’s claims filed on December 12, 2025 claims 1, 3, 5-8, 10, 12-15, 17, 19-20 are now pending for examination in the application.
Response to Arguments
This office action is in response to amendment filed 12/12/2025. In this action claim(s) 1, 3, 5-8, 10, 12-15, 17, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mazza et al. (US Patent No. 11676044) and Gurgu et al. (US Pub. No. 20230297887) in further view of Lange (US Pub. No. 20230259714). The Lange reference has been added to address the amendment of processing subsequent batches of examples from the training dataset until employing an incrementally optimized combinatorial prompt would yield a minimum loss value that is greater than or equal to a comparative loss value over the validation dataset associated with a most recent updated prompt.
Applicant’s arguments:
In regards to claim 1 on Page(s) 8, applicant argues “(The claim 1 includes steps and features that integrate the claimed technical advancements into a practical application. By using the recited steps to find the best prompt (e.g., to obtain the updated prompt having the lowest calculated loss value), a best prompt is used for tuning the language machine learning model. Appellant's specification identifies a technical problem. For example, in paragraph [0023] describes details about "challenges [for] engineering prompts for tuning large language models." The specification also describes how the method as recited in the amended claim 1 allows "for exploration of textual space more efficiently and systematically, thereby increasing accuracy and speed during performance of a variety of tasks." Fine tuning large language models is indeed a technical field. MPEP 2106.04(d) says that integration into a practical application may be shown by "claims that improve the functioning of a computer or other technology or technological field" (emphasis added), not only by claims that improve the functioning of a computer.”
Examiner’s Reply:
Applicant argues that the claims comprises statutory subject matter. Examiner respectfully disagrees. If a claim limitation, under its broadest reasonable interpretation, covers a mental process (eg prompting to answer questions)), then it falls within the “Mental process” grouping of abstract ideas set forth in the 2019 PEG. Accordingly, the claim recites an abstract idea. The examiner notes that the computer as recited in the claims are being used for question answering (the computer is being used as a generic tool). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application. Generating a combination of prompts to answer a question does not improve the functioning of a computing system.
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.
Claim 1, 3, 5-8, 10, 12-15, 17, 19-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1, 3, 5-8, 10, 12-15, 17, 19-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter 2019 PEG.
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the system and methods of claims 1, 3, 5-8, 10, 12-15, 17, 19-20are directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the mathematical concepts and mental processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 8, and 15 are directed towards the Mathematical Concepts and Mental Process Grouping of Abstract Ideas.
Independent claim 1, 8, and 15 recites the following limitations directed towards a Mental Processes:
selecting a predetermined number of examples from a training dataset (The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by combining prompts/phrases for answering questions);
concatenating each of the selected examples with the first prompt to obtain candidate prompts (The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by combining prompts/phrases for answering questions), See MPEP 2106.05(g).
for each of the candidate prompts, calculating a respective loss value over a validation dataset (The limitation recites a mathematical concept; calculating); and
replacing the first prompt with a first of the candidate prompts having a lowest calculated loss value that is less than or equal to an original loss value over the validation dataset for the first prompt to obtain an updated prompt (The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by combining prompts/phrases for answering questions).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 8, and 15:
one or more processors (i.e., as a generic processor performing a generic computer function),
one or more computer-readable memories (i.e., as a generic processor performing a generic computer function),
one or more computer-readable tangible storage medium (i.e., as a generic processor performing a generic computer function), and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories;
receiving a first prompt for a language machine learning model (recites insignificant extra solution activity that amounts to mere data gathering);
executing, by the target model, the individual candidate prompt having the lowest calculated loss value (recites insignificant extra solution activity that amounts to executing a prompt);
processing subsequent batches of examples from the training dataset until employing an incrementally optimized combinatorial prompt would yield a minimum loss value that is greater than or equal to a comparative loss value over the validation dataset associated with a most recent updated prompt (recites insignificant extra solution activity that amounts to batch processing).
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 8, and 15 are rejected under 35 U.S.C. 101.
With respect to claim(s) 3, 10, 17:
Step 2A, prong one of the 2019 PEG:
wherein the language machine learning model comprises a question- answering system (The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by combining prompts/phrases for answering questions).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of question/answering using designed prompts.
Step 2B Analysis:
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.
With respect to claim(s) 5, 12, 19:
performing centroid-based selection, based on geometrical techniques, of the predetermined number of examples (The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by combining prompts/phrases for answering questions).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of question/answering using designed prompts.
Step 2B Analysis:
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.
With respect to claim(s) 6, 13, 20:
performing a random search of the training dataset for hyperparameters comprising the predetermined number of examples (The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind by combining prompts/phrases for answering questions).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of question/answering using designed prompts.
Step 2B Analysis:
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.
With respect to claim(s) 7 and 14:
continuously calculating a minimum loss value for a most-updated prompt to provide a baseline loss value (The limitation recites a mathematical concept; calculating).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity of question/answering using designed prompts.
Step 2B Analysis:
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 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.
Claim(s) 1, 3, 5-8, 10, 12-15, 17, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mazza et al. (US Patent No. 11676044) and Gurgu et al. (US Pub. No. 20230297887) in further view of Lange (US Pub. No. 20230259714).
With respect to claim 1, Mazza et al. teaches a computer-implemented method comprising:
selecting a predetermined number of examples from a training dataset (See Column 5 Lines 23-32 discloses the chatbot system 10 provides recommendations of training data that may be used by the chatbot builder 12 to train the inference models used by the chatbot to respond to user queries. In this regard, the chatbot system 10 may analyze the knowledge base 14 for automatically identifying question and answer pairs that may be used as the training data);
receiving a first prompt for a language machine learning model (See Column 3 Lines 64-67 and Column 4 Lines 1-9 discloses source may include, for example, a company's website (e.g., a Frequently Asked Questions (FAQ) page, help page, etc.), other documents generated for the company (e.g., text documents, image files, sound files, etc.), social media postings, and/or the like (collectively referenced as source data). In one embodiment, the source data is segmented to generate one or more data blocks. The data blocks may then be provided to one or more machine learning models to generate questions for the input data blocks. In one embodiment, a large language model is leveraged to generate candidate questions given the source data);
concatenating each of the selected examples with the first prompt to obtain candidate prompts (See Column 3 Lines 64-67 and Column 4 Lines 1-9 discloses source may include, for example, a company's website (e.g., a Frequently Asked Questions (FAQ) page, help page, etc.), other documents generated for the company (e.g., text documents, image files, sound files, etc.), social media postings, and/or the like (collectively referenced as source data). In one embodiment, the source data is segmented to generate one or more data blocks. The data blocks may then be provided to one or more machine learning models to generate questions for the input data blocks. In one embodiment, a large language model is leveraged to generate candidate questions given the source data). Mazza et al. does not disclose replacing the first prompt with a first of the candidate prompts having a lowest calculated loss value that is less than or equal to an original loss value over the validation dataset for the first prompt to obtain an updated prompt.
However, Gurgu et al. teaches replacing the first prompt with a first of the candidate prompts having a lowest calculated loss value that is less than or equal to an original loss value over the validation dataset for the first prompt to obtain an updated prompt (Paragraph 97 discloses a hyperparameter of the language model may be altered. In some embodiments, the alteration may be of the prompt structure. In yet some embodiments, the current large language model may be replaced with a different large language model based on the computed metrics and Paragraph 98 discloses the metrics are computed based on received feedback for the question suggestions. For example, the question suggestions may be presented to the chatbot administrator. The chatbot administrator may be prompted to accept or reject the question suggestion, and label the suggestion accordingly);
executing, by the language machine learning model, the updated candidate prompt for tuning the language machine learning model (Paragraph 128 discloses the evaluation system 304 sets a test value for a parameter or hyperparameter (collectively referenced as parameter) of the large language model used to generate suggested training questions. For example, certain test values may be tried for one of the temperature, top-k, and/or top-p hyperparameters of the large language model. In some embodiments, the parameters to be tested may relate to the prompt structure. For example, certain test wording may be used for the prompt structure to determine its effectiveness in generating relevant training questions).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Mazza et al. with Gurgu et al. This would have facilitated improved question answering. See Gurgu et al. Paragraph(s) 3-19.
The Mazza et al. reference as modified by Gurgu et al. does not disclose for each of the candidate prompts, calculating a respective loss value over a validation dataset.
However, Lange discloses for each of the candidate prompts, calculating a respective loss value over a validation dataset (Paragraph 112 discloses GAIN system updates one or more model parameter values, for example weights and/or biases, of the language model, based on the computed loss, according to block 430);
processing subsequent batches of examples from the training dataset until employing an incrementally optimized combinatorial prompt would yield a minimum loss value that is greater than or equal to a comparative loss value over the validation dataset associated with a most recent updated prompt (Paragraph 101 disclose Training data for training the LM 120 can be one or more training examples, for example, a mini batch of training examples representing some subset of the total training data, or a set of training data).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Mazza et al. and Gurgu et al. with Lange. This would have facilitated improved question answering. See Lange Paragraph(s) 1-19.
The Mazza et al. reference as modified by Gurgu et al. and Lange teaches all the limitations of claim 1. With respect to claim 3, Gurgu et al. teaches the computer-implemented method of claim 1, wherein
the language machine learning model comprises a question-answering system (Paragraph 63 discloses automatic recommendation of question and answer pairs that may be used as the training data may help expedite the training of the chatbot). The motivation to combine statement previously provided in the rejection of dependent claim 3 provided above, combining the Mazza et al. reference and the Gurgu et al. reference is applicable to independent claim 1.
The Mazza et al. reference as modified by Gurgu et al. and Lange teaches all the limitations of claim 1. With respect to claim 5, Lange teaches the computer-implemented method of claim 1, wherein
selecting the predetermined number of examples from the training dataset further comprises:
performing centroid-based selection, based on geometrical techniques, of the
predetermined number of examples (Paragraph 16 discloses train the language model until reaching one or more convergence criteria, wherein in training the language model, the one or more processors are configured to perform one or more iterations of: sending, as input to the language model, a training example representing at least a portion of a session log labeled with an API call, the session log generated using the conversation graph, and computing a loss between a generated output of the language model from the training example, with the labeled API call, and updating one or more model parameter values of the language model based on the computed loss). The motivation to combine statement previously provided in the rejection of dependent claim 5 provided above, combining the Mazza et al. reference and the Lange reference is applicable to independent claim 1.
The Mazza et al. reference as modified by Gurgu et al. and Lange teaches all the limitations of claim 1. With respect to claim 6, Lange teaches the computer-implemented method of claim 1, wherein
selecting the predetermined number of examples from the training dataset further comprises:
performing a random search of the training dataset for hyperparameters comprising the
predetermined number of examples (Paragraph 60 discloses input parameters). The motivation to combine statement previously provided in the rejection of dependent claim 6 provided above, combining the Mazza et al. reference and the Lange reference is applicable to independent claim 1.
The Mazza et al. reference as modified by Gurgu et al. and Lange teaches all the limitations of claim 1. With respect to claim 7, Lange teaches the computer-implemented method of claim 1, further comprising:
continuously calculating a minimum loss value for a most-updated prompt to provide a baseline loss value (Paragraph 16 discloses train the language model until reaching one or more convergence criteria, wherein in training the language model, the one or more processors are configured to perform one or more iterations of: sending, as input to the language model, a training example representing at least a portion of a session log labeled with an API call, the session log generated using the conversation graph, and computing a loss between a generated output of the language model from the training example, with the labeled API call, and updating one or more model parameter values of the language model based on the computed loss). The motivation to combine statement previously provided in the rejection of dependent claim 7 provided above, combining the Mazza et al. reference and the Lange reference is applicable to independent claim 1.
With respect to claim 8, Mazza et al. teaches a computer system, the computer system comprising:
one or more processors (See Fig. 10), one or more computer-readable tangible storage medium (See Fig. 10), and program instructions stored on at least one of the one or more computer-readable tangible storage medium for execution by at least one of the one or more processors to perform operations comprising:
selecting a predetermined number of examples from a training dataset (See Column 5 Lines 23-32 discloses the chatbot system 10 provides recommendations of training data that may be used by the chatbot builder 12 to train the inference models used by the chatbot to respond to user queries. In this regard, the chatbot system 10 may analyze the knowledge base 14 for automatically identifying question and answer pairs that may be used as the training data);
receiving a first prompt for a language machine learning model (See Column 3 Lines 64-67 and Column 4 Lines 1-9 discloses source may include, for example, a company's website (e.g., a Frequently Asked Questions (FAQ) page, help page, etc.), other documents generated for the company (e.g., text documents, image files, sound files, etc.), social media postings, and/or the like (collectively referenced as source data). In one embodiment, the source data is segmented to generate one or more data blocks. The data blocks may then be provided to one or more machine learning models to generate questions for the input data blocks. In one embodiment, a large language model is leveraged to generate candidate questions given the source data);
concatenating each of the selected examples with the first prompt to obtain candidate prompts (See Column 3 Lines 64-67 and Column 4 Lines 1-9 discloses source may include, for example, a company's website (e.g., a Frequently Asked Questions (FAQ) page, help page, etc.), other documents generated for the company (e.g., text documents, image files, sound files, etc.), social media postings, and/or the like (collectively referenced as source data). In one embodiment, the source data is segmented to generate one or more data blocks. The data blocks may then be provided to one or more machine learning models to generate questions for the input data blocks. In one embodiment, a large language model is leveraged to generate candidate questions given the source data). Mazza et al. does not disclose replacing the first prompt with a first of the candidate prompts having a lowest calculated loss value that is less than or equal to an original loss value over the validation dataset for the first prompt to obtain an updated prompt.
However, Gurgu et al. teaches replacing the first prompt with a first of the candidate prompts having a lowest calculated loss value that is less than or equal to an original loss value over the validation dataset for the first prompt to obtain an updated prompt (Paragraph 97 discloses a hyperparameter of the language model may be altered. In some embodiments, the alteration may be of the prompt structure. In yet some embodiments, the current large language model may be replaced with a different large language model based on the computed metrics and Paragraph 98 discloses the metrics are computed based on received feedback for the question suggestions. For example, the question suggestions may be presented to the chatbot administrator. The chatbot administrator may be prompted to accept or reject the question suggestion, and label the suggestion accordingly);
executing, by the language machine learning model, the updated candidate prompt for tuning the language machine learning model (Paragraph 128 discloses the evaluation system 304 sets a test value for a parameter or hyperparameter (collectively referenced as parameter) of the large language model used to generate suggested training questions. For example, certain test values may be tried for one of the temperature, top-k, and/or top-p hyperparameters of the large language model. In some embodiments, the parameters to be tested may relate to the prompt structure. For example, certain test wording may be used for the prompt structure to determine its effectiveness in generating relevant training questions).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Mazza et al. with Gurgu et al. This would have facilitated improved question answering. See Gurgu et al. Paragraph(s) 3-19.
The Mazza et al. reference as modified by Gurgu et al. does not disclose for each of the candidate prompts, calculating a respective loss value over a validation dataset.
However, Lange discloses for each of the candidate prompts, calculating a respective loss value over a validation dataset (Paragraph 112 discloses GAIN system updates one or more model parameter values, for example weights and/or biases, of the language model, based on the computed loss, according to block 430);
processing subsequent batches of examples from the training dataset until employing an incrementally optimized combinatorial prompt would yield a minimum loss value that is greater than or equal to a comparative loss value over the validation dataset associated with a most recent updated prompt (Paragraph 101 disclose Training data for training the LM 120 can be one or more training examples, for example, a mini batch of training examples representing some subset of the total training data, or a set of training data).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Mazza et al. and Gurgu et al. with Lange. This would have facilitated improved question answering. See Lange Paragraph(s) 1-19.
With respect to claim 10, it is rejected on grounds corresponding to above rejected claim 3, because claim 10 is substantially equivalent to claim 3.
With respect to claim 12, it is rejected on grounds corresponding to above rejected claim 5, because claim 12 is substantially equivalent to claim 5.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 6, because claim 13 is substantially equivalent to claim 6.
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 7, because claim 14 is substantially equivalent to claim 7.
With respect to claim 15, Mazza et al. teaches a computer program product comprising:
a computer-readable tangible storage medium and program instructions stored on the computer-readable tangible storage medium (See Fig. 10), to perform operations comprising:
selecting a predetermined number of examples from a training dataset (See Column 5 Lines 23-32 discloses the chatbot system 10 provides recommendations of training data that may be used by the chatbot builder 12 to train the inference models used by the chatbot to respond to user queries. In this regard, the chatbot system 10 may analyze the knowledge base 14 for automatically identifying question and answer pairs that may be used as the training data);
receiving a first prompt for a language machine learning model (See Column 3 Lines 64-67 and Column 4 Lines 1-9 discloses source may include, for example, a company's website (e.g., a Frequently Asked Questions (FAQ) page, help page, etc.), other documents generated for the company (e.g., text documents, image files, sound files, etc.), social media postings, and/or the like (collectively referenced as source data). In one embodiment, the source data is segmented to generate one or more data blocks. The data blocks may then be provided to one or more machine learning models to generate questions for the input data blocks. In one embodiment, a large language model is leveraged to generate candidate questions given the source data);
concatenating each of the selected examples with the first prompt to obtain candidate prompts (See Column 3 Lines 64-67 and Column 4 Lines 1-9 discloses source may include, for example, a company's website (e.g., a Frequently Asked Questions (FAQ) page, help page, etc.), other documents generated for the company (e.g., text documents, image files, sound files, etc.), social media postings, and/or the like (collectively referenced as source data). In one embodiment, the source data is segmented to generate one or more data blocks. The data blocks may then be provided to one or more machine learning models to generate questions for the input data blocks. In one embodiment, a large language model is leveraged to generate candidate questions given the source data). Mazza et al. does not disclose replacing the first prompt with a first of the candidate prompts having a lowest calculated loss value that is less than or equal to an original loss value over the validation dataset for the first prompt to obtain an updated prompt.
However, Gurgu et al. teaches replacing the first prompt with a first of the candidate prompts having a lowest calculated loss value that is less than or equal to an original loss value over the validation dataset for the first prompt to obtain an updated prompt (Paragraph 97 discloses a hyperparameter of the language model may be altered. In some embodiments, the alteration may be of the prompt structure. In yet some embodiments, the current large language model may be replaced with a different large language model based on the computed metrics and Paragraph 98 discloses the metrics are computed based on received feedback for the question suggestions. For example, the question suggestions may be presented to the chatbot administrator. The chatbot administrator may be prompted to accept or reject the question suggestion, and label the suggestion accordingly);
executing, by the language machine learning model, the updated candidate prompt for tuning the language machine learning model (Paragraph 128 discloses the evaluation system 304 sets a test value for a parameter or hyperparameter (collectively referenced as parameter) of the large language model used to generate suggested training questions. For example, certain test values may be tried for one of the temperature, top-k, and/or top-p hyperparameters of the large language model. In some embodiments, the parameters to be tested may relate to the prompt structure. For example, certain test wording may be used for the prompt structure to determine its effectiveness in generating relevant training questions).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Mazza et al. with Gurgu et al. This would have facilitated improved question answering. See Gurgu et al. Paragraph(s) 3-19.
The Mazza et al. reference as modified by Gurgu et al. does not disclose for each of the candidate prompts, calculating a respective loss value over a validation dataset.
However, Lange discloses for each of the candidate prompts, calculating a respective loss value over a validation dataset (Paragraph 112 discloses GAIN system updates one or more model parameter values, for example weights and/or biases, of the language model, based on the computed loss, according to block 430);
processing subsequent batches of examples from the training dataset until employing an incrementally optimized combinatorial prompt would yield a minimum loss value that is greater than or equal to a comparative loss value over the validation dataset associated with a most recent updated prompt (Paragraph 101 disclose Training data for training the LM 120 can be one or more training examples, for example, a mini batch of training examples representing some subset of the total training data, or a set of training data).
Therefore, it would have been obvious at the time the invention was made to a person having ordinary skill in the art to modify Mazza et al. and Gurgu et al. with Lange. This would have facilitated improved question answering. See Lange Paragraph(s) 1-19.
With respect to claim 17, it is rejected on grounds corresponding to above rejected claim 3, because claim 17 is substantially equivalent to claim 3.
With respect to claim 19, it is rejected on grounds corresponding to above rejected claim 5, because claim 19 is substantially equivalent to claim 5.
With respect to claim 20, it is rejected on grounds corresponding to above rejected claim 6, because claim 20 is substantially equivalent to claim 6.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-Pub. No. 20180063062 is directed to Prompt ranking: [0006 & 0007] a machine-learning trained classifier may be used to optimize a predictor function selecting the types of prompt notifications that will most likely result in the user positively responding to the prompt (e.g., post content). Classification may be performed using a predictor function that is constructed using a set of “training” data that includes an input vector and an answer vector. The feature vector maps the values of the aforementioned posting features (e.g., post rates, demographics, location, interests) and dismissal features (e.g., close rate) for a particular user to a n-dimensional feature vector. The answer vector may be a vector of the result of the prompt notification (e.g., whether or not the user posted content or dismissed the prompt notification). The learned association of the machine-learning classifier may be used to optimize the set of weights of the linear predictor function. In particular embodiments, the predictor function may be a weighted function of the posting probability, dismissal probability, and impressions (the number of times a post is displayed). The result of the prompt notifications sent to the user and the subsequent response to the user to the sent prompts may be logged and used as additional training data for the machine-language classifier to further refine the value of the set of weights. In particular embodiments, the candidate prompt notifications may be ranked in accordance with the respective value of the linear predictor function for each of the candidate prompt notifications using the values of the features for the particular user.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.E.A/Examiner, Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154