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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-2, 4-13, and 15-22 have been considered but are moot in view of the new grounds of rejection necessitated by the applicant’s amendments to the claims.
However, the examiner will respond to the applicant’s arguments, with respect to 35 U.S.C. 101.
With respect to step 2A, prong one, the applicant argues:
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This argument is not persuasive because there is a distinction between obtaining predicted character probabilities from a trained neural network, as opposed to receiving a natural language rule that may just be a single English sentence, as shown in figure 6 of the applicant’s drawings. Probabilities may be a result of complex mathematics that are not able to be contained in the human mind. Using simple natural language sentences is able to be performed in the human mind.
With respect to step 2A, prong two, the applicant argues:
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These arguments are not persuasive because unlike Desjardins, the current claims do not give detail to how the training is performed and/or how the training executes the improvement.
Please compare the following limitations:
(from Ex parte Desjardins) training the machine learning model on the second machine learning task by training the machine learning model on the second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task (emphasis mine)
(applicant’s current claim 1) training, by the one or more processors, the machine learning model based at least in part on the labeled training dataset in order to satisfy the performance condition associated with the user input
The training represented by the applicant’s limitation is more generic and does not explain how the machine learning model itself operates, like in Desjardins.
Upon performing an update search, it appears that the general principle of inputting natural language rules to a generic machine learning model is well-understood, routine, and conventional. For example, most, if not all, LLM interfaces use a natural language to machine learning/AI data input interface. Simply combining a general natural language input interface with a machine learning model is not considered to represent an improved technique for training the machine learning model, absent the details of how the model is trained.
The rejection is maintained.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 1000. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
In the applicant’s specification amendment of 01/16/26, the applicant stated: “Applicant submits that element 1000 is already mentioned in the description in paragraph [0152].” However, this does not appear to be the case. It appears that there is a reference 100 but not a reference 1000.
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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 1-2, 4-13, and 15-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
With respect to step 1 of the patent subject matter eligibility analysis, the claims are directed to a process, machine, manufacture, or composition of matter. Independent claim 1 is directed to a computer-implemented method, which is a process. Independent claim 12 is directed to a computing apparatus, which is a machine. Independent claim 18 is directed to a non-transitory computer storage medium, which is a manufacture. All other claims depend on independent claims 1, 12, and 18. As such, claims 1-20 are directed to a statutory category.
With respect to step 2A, prong one, the claims recite an abstract idea, law of nature, or natural phenomenon. Specifically, the following limitations recite mathematical concepts and/or mental processes.
Claim 1
providing a plurality of natural language rules, wherein a first natural language rule of the plurality of natural language rules (i) comprises a sequence of natural language words or phrases and (ii) describes a user interpretable rule corresponding to a performance condition for a machine learning model (This limitation recites an abstract mental process. Paragraph 0096 of the applicant’s original specification gives examples of what constitutes a natural language rule. It states, “The natural language text may form one or more natural language rules including, for example, a first language rule 404 and a second natural language rule 406.” References 404 and 406 in figures 4-6 are rules that constitute one or two sentences. Providing one or two sentences as natural language rules is a concept that can be performed in the human mind.)
receiving a user input comprising (i) a second natural language rule not present in the plurality of natural language rules or (ii) a modification to the first natural language rule (This recites an observation, evaluation, judgment, and/or opinion that can be performed in the human mind. For example, reading the second natural language rule could be a form of “receiving” the user input.)
Independent claims 12 and 18 recite similar abstract ideas as those recited in claim 1.
All other claims depend on independent claims 1, 12, and 18. They also recite its abstract limitations by virtue of their dependence. In addition, some of the claims also recite their own abstract ideas.
Claims 2 and 13 disclose the generation of performance metrics, which represent an abstract mathematical relationship between the machine learning model and its performance.
Claims 4 and 15 disclose modifying a third natural language rule of the plurality of natural language rules based at least in part on the evaluation data. Performing a simple rule modification based on data is an abstract mental process. For example, editing a sentence is an operation that can be performed in the human mind.
Claims 5 and 16 disclose that the evaluation data is indicative of a relative performance of the machine learning model relative to a previous model generated without the user input. This is a simple observation, evaluation, judgment, and/or opinion that can be performed in the human mind by comparing one performance metric to another.
Claims 6 and 17 disclose that the second natural language rule is generated based at least in part on a particular structured language rule of the plurality of structured language rules. This is a mental process that can be performed in the human mind.
Claims 7 and 19 disclose correlations between the plurality of computer interpretable rules and the plurality of natural language rules. Such correlations represent mathematical relationships. The claims therefore recite abstract mathematical concepts.
Claim 9 discloses identifying a rule attribute, generating a real time label for the rule attribute, and modifying the sequence of natural language words or phrases to identify the real time label. Paragraph 0037 of the applicant’s original specification states, “A rule attribute, for example, may correspond to a portion of text from a natural language rule and/or a portion of code from a structured language rule.” The claimed identifying, generating, and modifying are all abstract mental concepts that can be performed in the human mind.
Claim 10 discloses a identifying a computer interpretable template corresponding to the rule attribute. Paragraph 0036 of the applicant’s original specification states, “the term ‘computer-interpretable template’ refers to a data entity that describes a predefined template for generating a computer interpretable rule based on one or more rule attributes. The computer interpretable template may include a segment of program code … the segment of program code may include an executable if-then statement.” Identifying a segment of program code and then generating a rule based at least in part on it and a rule attribute is an abstract mental process that can be performed in the human mind.
With respect to step 2A, prong two, the claims do not recite additional elements that integrate the judicial exception into a practical application. The following limitations are considered “additional elements” and explanation will be given as to why these “additional elements” do not integrate the judicial exception into a practical application.
Claim 1
A computer-implemented method (Merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
providing for display, by one or more processors and via an interactive user interface (A general and generic display of information, via a generic interactive user interface, merely adds insignificant extra-solution activity to the judicial exception and is not indicative of integration into a practical application (see MPEP 2106.05(g)). Furthermore, mere instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application (see MPEP 2106.05(f)).)
by the one or more processors (Merely using a computer as a tool to perform an abstract idea is not indicative of integration into a practical application.)
via an interactive user interface (A general and generic disclosure of an interactive user interface merely adds insignificant extra-solution activity to the judicial exception. It also serves to generally link the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). It is not indicative of integration into a practical application.)
inputting, by the one or more processors, the user input to a natural language model to receive a computer interpretable rule corresponding to the user input, wherein the computer interpretable rule comprises a segment of program code that is executable to perform a labeling function for a training dataset that corresponds to the performance condition (This limitation transmits data to a model. It is not indicative of integration into a practical application because it merely implements an abstract idea on a computer or uses a computer as a tool to perform an abstract idea.)
inputting, by the one or more processors, the computer interpretable rule to a weak supervision model to receive a labeled training dataset (This limitation transmits data to a model. It is not indicative of integration into a practical application because it merely implements an abstract idea on a computer or uses a computer as a tool to perform an abstract idea.)
training, by the one or more processors, the machine learning model based at least in part on the labeled training dataset in order to satisfy the performance condition associated with the user input (Here, the limitation does not detail the nature of the training. It merely states that the training is done. This limitation merely uses a computer as a tool to perform an abstract idea.)
rendering, by the one or more processors and via the interactive user interface, evaluation data for the machine learning model (Here, the limitation does not detail the nature of the rendering. It merely states that the rendering is done. This limitation merely uses a computer as a tool to perform an abstract idea.)
Independent claims 12 and 18 recite similar limitations that are not indicative of integration into a practical application as those recited in claim 1. Independent claims 12 and 18 also recite additional computer elements, such as memory, program code, and non-transitory computer storage medium that also merely use a computer as a tool to perform an abstract idea.
All other claims depend on independent claims 1, 12, and 18. They also recite its limitations that are not indicative of integration into a practical application, by virtue of their dependence.
In addition, claim 8 discloses storing the computer interpretable rule in association with the second natural language rule ion the rule database. This also merely uses a computer as a tool to perform an abstract idea. It is not indicative of integration into a practical application.
With respect to step 2B, the claims do not recite additional elements that amount to significantly more than the judicial exception. The claimed invention does not add significantly more because, as discussed above in step 2A, prong two, the claims do nothing more than merely use a computer as a tool to perform an abstract idea; add insignificant extra-solution activity to the judicial exception; and/or generally link the use of the judicial exception to a particular technological environment or field of use. The claims are directed to receiving and processing data. This is well-understood, routine, and conventional. Simply appending well-understood, routine, and conventional activities previously known to the industry, and specified at a high level of generality, to the judicial exception is not indicative of an inventive concept (aka “significantly more”) (see MPEP 2106.05(d) and Berkheimer Memo).
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) 1-2, 4-8, 12-13, and 15-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (US PgPub 20220269862) in view of Smith et al (US PgPub 20240160900).
With respect to claim 1, Li et al discloses:
A computer-implemented method, the computer-implemented method (paragraphs 0001-0002 state, “The present invention relates to systems and methods for performing named-entity recognition (NER) using machine-learning techniques and, more specifically, for training named-entity recognition (NER) models. Named-entity recognition (NER) is a mechanism in which automated processing (e.g., computer-based processing) is applied to unstructured text in order to identify and categorize occurrences of ‘named entities’ (e.g., people, businesses, locations, etc.) in the unstructured text.” Please note that paragraph 0042 of the applicant’s original specification states, “In some embodiments, the machine learning natural language model may include a name entity recognition model.”) comprising:
providing for display, by one or more processors and via an interactive user interface (figure 1, reference 105 discloses display; figure 1, reference 107 discloses user input device; paragraph 0015 states, “The electronic processor 101 is communicative coupled to a display 105 and a user input device 107 … to provide a user interface for operating the system 100 and for displaying data to a user.”), a plurality of natural language rules, wherein a first natural language rule of the plurality of natural language rules (i) comprises a sequence of natural language words or phrases and (ii) describes a user interpretable rule corresponding to a performance condition for a machine learning model (figure 4, reference 419; paragraph 0002 states, “NER is a machine-learning-based natural language processing mechanism …”; paragraph 0005 states, “The automatically identified rules are applied to the unstructured text and the text/label combinations determined by the rules are compared to the text/label combinations determined by the initial iteration of the NER model. The most successful ‘rules’ are identified using a scoring metric and are then applied to the original unstructured text to generate another set of training data.”)
With respect to claim 1, Li et al differs from the claimed invention in that it does not explicitly disclose:
receiving, by the one or more processors and via the interactive user interface, a user input comprising (i) a second natural language rule not present in the plurality of natural language rules or (ii) a modification to the first natural language rule
inputting, by the one or more processors, the user input to a natural language model to receive a computer interpretable rule corresponding to the user input, wherein the computer interpretable rule comprises a segment of program code that is executable to perform a labeling function for a training dataset that corresponds to the performance condition
inputting, by the one or more processors, the computer interpretable rule to a weak supervision model to receive a labeled training dataset
training, by the one or more processors, the machine learning model based at least in part on the labeled training dataset in order to satisfy the performance condition associated with the user input
rendering, by the one or more processors and via the interactive user interface, evaluation data for the machine learning model
With respect to claim 1, Smith discloses:
receiving, by the one or more processors and via the interactive user interface, a user input comprising (i) a second natural language rule not present in the plurality of natural language rules or (ii) a modification to the first natural language rule (paragraphs 0094-0098 state, “A weak supervision interaction model … may be extended to other modalities or tasks, including supervised tasks with natural language, and generating labeling functions automatically … Once a user has ‘written’ or defined a labeling function, no additional human effort is required to label the data … When a user produces labels programmatically, recreating the training labels is as simple as adding or modifying a small, targeted number of labeling functions and re-executing them, which can occur at computing speed, not human speed.” The claimed user input is analogous to the adding or modifying principles taught by Smith.)
inputting, by the one or more processors, the user input to a natural language model to receive a computer interpretable rule corresponding to the user input, wherein the computer interpretable rule comprises a segment of program code that is executable to perform a labeling function for a training dataset that corresponds to the performance condition (obvious in view of combination; Li et al discloses weakly-supervised training of a machine-learning model. Smith discloses user input that allows for modifications or re-writes of the rules in natural language. Transmitting data to a model is obvious to both Li and Smith, as it is obvious computer processing activity, in view of the collecting teachings of Li, in view of Smith.)
inputting, by the one or more processors, the computer interpretable rule to a weak supervision model to receive a labeled training dataset (obvious in view of combination; Li et al discloses weakly-supervised training of a machine-learning model. Smith discloses user input that allows for modifications or re-writes of the rules in natural language. Transmitting data to a model is obvious to both Li and Smith, as it is obvious computer processing activity, in view of the collecting teachings of Li, in view of Smith.)
training, by the one or more processors, the machine learning model based at least in part on the labeled training dataset in order to satisfy the performance condition associated with the user input (obvious in view of combination; Li et al discloses weakly-supervised training of a machine-learning model. Smith discloses user input that allows for modifications or re-writes of the rules in natural language.)
rendering, by the one or more processors and via the interactive user interface, evaluation data for the machine learning model (obvious in view of combination; Both Li and Smith disclose display of data. Rendering data, so that it displays properly would be obvious to one of ordinary skill in the art.
With respect to claim 1, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Smith et al into the invention of Li et al. The motivation for the skilled artisan in doing so is to gain the benefit of allowing users to not only create training rules but also to modify them, using natural language.
Independent claim 12 discloses a computing apparatus variation of the computer-implemented method of claim 1. It is rejected for similar reasons as those given with respect to claim 1 above. The main difference between the claims is in the preamble, where the preamble of claim 12 is also anticipated by Li et al:
A system comprising: one or more processors; and at least one memory storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to: (figure 1, reference 101 discloses processor and reference 103 discloses memory)
Independent claim 18 discloses a non-transitory computer storage medium variation of the computer-implemented method of claim 1. It is rejected for similar reasons as those given with respect to claim 1 above. The main difference between the claims is in the preamble, where the preamble of claim 18 is also anticipated by Li et al:
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to: (suggested by processor (reference 101) and memory (reference 103) in figure 1 of Li et al.)
With respect to claim 2, Li et al, as modified, discloses:
generating, by the one or more processors and using the machine learning model, one or more performance metrics for the machine learning model (Li figure 4, references 413 and 419; paragraph 0022 states, “the accuracy of each rule candidate is scored …” Under broadest reasonable interpretation (BRI), accuracy is considered a performance metric.)
generating, by the one or more processors, the evaluation data for the machine learning model based at least in part on the one or more performance metrics, wherein the evaluation data is indicative of an association between the user input and the one or more performance metrics (obvious in view of combination; Li paragraph 0022 states, “The system identifies the top performing rule candidates (e.g., the rule candidates that produce labels that most accurately match the set of ‘predicted data’ labels produced by the neural NER model) …” Under BRI, if accuracy is the performance metric, the rule candidates are interpretated to anticipate the claimed evaluation data.)
Claim 13 is rejected for similar reasons as claim 2 above.
With respect to claim 4, Li et al, as modified, discloses:
modifying a third natural language rule of the plurality of natural language rules based at least in part on the evaluation data (obvious in view of combination; Smith’s teachings of user modifying rules (paragraph 0098) supports any number of modified natural language rules.)
Claim 15 is rejected for similar reasons as claim 4 above.
With respect to claim 5, Li et al, as modified, discloses:
wherein the machine learning model is generated based at least in part on a plurality of natural language rules, wherein the evaluation data is indicative of a relative performance of the machine learning model relative to a previous model generated without the user input (obvious in view of combination; Li paragraph 0005 states, “The automatically identified rules are applied to the unstructured text and the text/label combinations determined by the rules are compared to the text/label combinations determined by the initial iteration of the NER model … The NER model is then retrained based on the data as labeled by the new set of selected rules. This training process is iteratively repeated to continue to refine and improve the NER model.” Please compare this disclosure with paragraph 0140 of the applicant’s original specification, which states, “At each iteration, a new predictive machine learning model may be generated based on a plurality of natural language rules. In some embodiments, the evaluation data provided at the conclusion of an iteration is indicative of a relative performance of the predictive machine learning model relative to a previous model generated without one or more of the natural language rules.” Li et al, like the applicant, discloses an iterative process. Also, as discussed above, Smith teaches modified rules. Comparing performance of modified rules with previous rules is obvious to one of ordinary skill in the art.)
Claim 16 is rejected for similar reasons as claim 5 above.
With respect to claim 6, Li et al, as modified, discloses:
wherein the previous model is a rules-based model defined by a plurality of structured language rules (Li figure 3 shows a structure of the rules-based model)
and the computer-implemented method further comprises: generating, by the one or more processors, the second natural language rule based at least in part on a particular structured language rule of the plurality of structured language rules (obvious in view of combination; Li figure 3, reference 317 shows a rule selector; Li figure 4, reference 415 shows “Select Top Performing Rule Candidates …”; Selection suggests particularity. Smith teaches second natural language rule, as discussed above.)
Claim 17 is rejected for similar reasons as claim 6 above.
With respect to claim 7, Li et al, as modified, discloses:
wherein the first natural language rule is selected from a rule database associated with a plurality of predictive models (Although Li et al does not explicitly use the word “database,” a database is obvious in view of the “set of seeding rules” and the selection from the set of rule candidates.)
wherein the rule database comprises data indicative of:
(i) a plurality of computer interpretable rules and the plurality of natural language rules (discussed with respect to Named-entity recognition (NER) above; see Li paragraphs 0002-0008), and
(ii) one or more rule associations that identify one or more correlations between the plurality of computer interpretable rules and the plurality of natural language rules (Li paragraph 0002 states, NER is a machine-learning-based natural language processing mechanism in which unstructured natural-language sentences are provided as input to a machine-learning model and the output of the machine-learning model includes an indication of an assigned category for each ‘entity’ (or potential entity) in the sentence … For example, if the input sentence provided to as input recites: ‘John is travelling to London,’ the output of a trained NER machine-learning model may indicate that ‘John’ is categorized as a ‘person’ and ‘London’ is categorized as a ‘location.’”)
Claim 19 is rejected for similar reasons as claim 7 above.
With respect to claim 8, Li et al, as modified, discloses:
further comprising: storing, by the one or more processors, the computer interpretable rule in association with the second natural language rule in the rule database (obvious in view of disclosure of memory in Li figure 1, reference 103 and the structured NER framework shown in Li figure 3; As discussed above, Smith discloses second natural language rule.)
Claim 20 is rejected for similar reasons as claim 8 above.
With respect to claims 21-22, Li et al, as modified, discloses:
modify a third natural language rule of the plurality of natural language rules based at least in part on the evaluation data (obvious in view of combination; as discussed above, Smith discloses modifying natural language rules, which suggest any number of modifications.)
Claim(s) 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al (US PgPub 20220269862) in view of Smith et al (US PgPub 20240160900), as applied to claims 1-2, 4-8, 12-13, and 15-22 above, and further in view of Matsuoka et al (US PgPub 20230064816).
With respect to claim 9, Li et al, as modified, discloses:
The computer-implemented method of claim 1 (as applied to claim 1 above)
identifying, by the one or more processors and using the natural language model, a rule attribute based at least in part on the sequence of natural language words or phrases (paragraph 0028 states, “For example, in some biomedical domain datasets, prefix and suffix rules are more efficient rule templates than part-of-speech tags … the framework illustrated in the example of FIG. 3 above allows the user to customize their rule templates according to their dataset and domain.” Based on broadest reasonable interpretation (BRI), context sensitive rule templates are broadly interpreted to anticipate the claimed rule attributes.)
With respect to claim 9, Li et al, as modified, differs from the claimed invention in that it does not explicitly disclose:
generating, by the one or more processors, a real time label for the rule attribute
modifying, by the one or more processors and via the interactive user interface, the sequence of natural language words or phrases to identify the real time label
With respect to claim 9, Matsuoka et al discloses:
generating, by the one or more processors, a real time label for the rule attribute (Paragraph 0041 states, “the machine learning algorithm or other artificial intelligence can be dynamically trained in real-time …” Paragraph 0098 states, “methods of performing language processing … including, but not limited to, those based on named entity recognition … semantic role labelling … may be used to obtain information from the messages 122-124 and/or other such communications … and to provide analysis of those communications to the active task processing machine learning algorithm 308.” Matsuoka et al discloses real time processing of data in a machine learning and artificial intelligence context, including in training. As seen, it also recognizes the context of named entity recognition. It would be obvious to apply the real time principles of Matsuoka et al to the base NER teachings of Li et al, in order to arrive at the claimed invention.)
modifying, by the one or more processors and via the interactive user interface, the sequence of natural language words or phrases to identify the real time label (obvious in view of combination; paragraph 0211 of Matsuoka et al states, “Based on this evaluation, the machine learning model may be modified to increase the likelihood of the machine learning model identifying the desired correlations.” As discussed, Matsuoka et al also discusses real-time processing of data in a machine learning/artificial intelligence context. Modifications, with respect to Li et al were discussed above.)
With respect to claim 9, it would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Matsuoka et al into the invention of Li et al. The motivation for the skilled artisan in doing so is to gain the benefit of more efficiently processing data and ensuring that the data that is processed is up to date.
With respect to claim 10, Li et al, as modified, discloses:
wherein generating the computer interpretable rule comprises: identifying, by the one or more processors, a computer interpretable template corresponding to the rule attribute (discussed with respect to Li et al above; For example, paragraph 0028 discloses various domain datasets that form computer interpretable templates corresponding to different rule attributes, such as prefix and suffix rules, as opposed to part-of-speech tags.)
generating, by the one or more processors, the computer interpretable rule based at least in part on the rule attribute and the computer interpretable template (discussed with respect to Li et al above; see, for example, paragraphs 0028-0029)
With respect to claim 11, Li et al, as modified, discloses:
receiving, by the one or more processors and via the interactive user interface, labeling input comprising a label modification for the real time label (obvious in view of incorporating real time teachings of Matsuoka et al into the base NER teachings of Li et al)
modifying, by the one or more processors, the rule attribute corresponding to the real time label based at least in part on the label modification (obvious in view of incorporating real time teachings of Matsuoka et al into the base NER teachings of Li et al)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Poms et al (US PgPub 20230419121) discloses systems and methods for programmatic labeling of training data for machine learning models via clustering.
Mezaoui et al (US PgPub 20210034812) discloses methods and systems for multi-label classification of text data.
Schwabe et al (US PgPub 20220019608) discloses usability in information retrieval systems.
Hatamizadeh et al (US PgPub 20230145535) discloses neural network training technique.
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 LEONARD S LIANG whose telephone number is (571)272-2148. The examiner can normally be reached M-F 10:00 AM - 7 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, ARLEEN M VAZQUEZ can be reached at (571)272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEONARD S LIANG/ Examiner, Art Unit 2857 05/22/26
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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