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 Amendment
This action is in response to applicant’s arguments and amendments filed 2/13/2026, which are in response to USPTO Office Action mailed 11/26/2025. Applicant’s arguments have been considered with the results that follow: THIS ACTION IS MADE FINAL.
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, 3-4, 9, 11-14, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiao et al. (US PGPUB No. 2019/0180852; Pub. Date: Jun. 13, 2019) in view of TREHAN (US PGPUB No.: 2021/0350073; Pub. Date: Nov. 11, 2021) and Dharmasiri et al. (US PGPUB No. 2023/0095673; Pub. Date: Mar. 30, 2023).
Regarding independent claim 1,
Jiao discloses a computer-implemented method comprising; receiving, by one or more processors, an interaction data object including text data related to one or more interaction; See Paragraph [0028], (Disclosing a system for implementing a health verification and prediction service by generating in interactive user interface that allows user to provide responses to health assertions. The system may organize a library of health assertions and/or questions associated with one or more topics. The topics may be utilized to select assertions or questions that may be presented to a user for them to answer such as via a trivia session, i.e. receiving, by one or more processors, an interaction data object including text data related to one or more interaction.)
generating, by the one or more processors, an intent data object that includes a high level intent indicator and a granular intent indicator for the one or more interactions by applying the interaction data object to a trained intent classification machine-learning model; See FIG. 3 & See Paragraph [0094], (The system may generate a data structure that can be developed to link a question with a health outcome (e.g. a granular intent) and a topic (e.g. a high-level intent). FIG. 3 illustrates data structure 300 is configured to store associations between topics and questions.) See Paragraph [0087], (Machine-learning models may be used to develop corrective health parameters and determining health outcomes based on correlative health parameters, i.e. generating, by the one or more processors, an intent data object that includes a high level intent indicator and a granular intent indicator for the one or more interactions by applying the interaction data object to a trained intent classification machine-learning model.)
generating, by the one or more processors, a subject data object for the one or more interactions by applying the interaction data object to a trained subject classification machine-learning model, the subject data object including a subject indicator; See Paragraph [0176], (A lifestyle categorization may be inferred from social network content of a user.) See Paragraphs [0186] & [0193], (User categorization system 1102 may implement machine learning models such as a neural network or random forest, to determine one or more lifestyle categories for a user. User categorization system 1102 may make predictions about a user's attributes based on assertions a user may make as determined from answering questions on various health topics, i.e. generating, by the one or more processors, a subject data object for the one or more interactions by applying the interaction data object to a trained subject classification machine-learning model, the subject data object including a subject indicator (e.g. the system may infer characteristics of a user that correspond to categories).)
Jiao does not disclose the step of selecting, by the one or more processors, a target model bundle from a plurality of model bundles by using a predefined mapping, between different granular intent indicators and corresponding model bundles of the plurality of model bundles, to identify the target model bundle based on the granular intent indicator, wherein: the target model bundle includes a plurality of machine-learning models trained to extract one or more signals from the text data;
TREHAN discloses the step of selecting, by the one or more processors, a target model bundle from a plurality of model bundles by using a predefined mapping, between different granular intent indicators and corresponding model bundles of the plurality of model bundles, to identify the target model bundle based on the granular intent indicator, wherein: the target model bundle includes a plurality of machine-learning models trained to extract one or more signals from the text data; See Paragraph [0010], (Disclosing a system for processing a user input such as a user input text using Natural Language Processing (NLP) techniques. The system may match a user input to a set of input intent maps generated from predefined training inputs.) See Paragraph [0045]-[0046], (The system comprises a distance determination module 212 configured to determine a distance of each of a set of input intent maps relative to a plurality of pre-stored sets of intent maps. Based on the distance, the pre-stored intent map identification module may identify a pre-stored intent map closest to the set of input intent maps, i.e. selecting, by the one or more processors, a target model bundle from a plurality of model bundles by using a predefined mapping (e.g. the system maps input intent maps to pre-stored intent maps), between different granular intent indicators (e.g. Note [0026] wherein intent maps correspond to a network of words, concepts, set of related words, fragments of a sentence, a set of sentences of a known domain, etc.) and corresponding model bundles of the plurality of model bundles (e.g. the system may determine a set of intent maps, i.e. model bundles), to identify the target model bundle based on the granular intent indicator (e.g. the corresponding pre-stored sets of intent maps are determined according to a distance metric computed that indicates a contextual similarity between the contents of an input and the pre-stored intent maps).) See Paragraph [0032], (Each of the pre-stored sets of indent maps are generated from a single predefined training input based on an iterative and elastic stretching process such that the pre-stored sets of intent maps may be gradually manipulated and stretched via intent map transforming algorithms. Note [0033] wherein the sets of pre-stored intent maps are matched to a user input in order to render the predefined response mapped to a particular pre-stored intent map. For example, a predetermined response may be delivered to a user based on the user's intent and context, i.e. wherein: the target model bundle includes a plurality of machine-learning models trained to extract one or more signals from the text data (e.g. the user input text is used to generate the response based on elements of the extracted user input such as keywords).)
Jiao and TREHAN are analogous art because they are in the same field of endeavor, document processing via machine learning tools. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Jiao to include the method of matching intent maps to a user request as disclosed by TREHAN. Paragraph [0036] of TREHAN discloses that the method of transforming intent maps for identifying and providing a closes pre-stored intent map improves the accuracy of match between the set of input intent maps and the plurality of pre-stored sets of intent maps.
Jiao-TREHAN does not disclose the step of modifying, by the one or more processors, a curated data object by changing one or more data entries based on (i) the generated signal object or (ii) the generated signal object and one or more of the generated intent data object or the generated subject data object.
Dharmasiri discloses the step of and individual machine-learning models of the target model bundle are trained to extract unique signal types from the text data related to the one or more interactions; See Paragraph [0007], (Disclosing techniques for extracting key information from a document using machine learning models in a chatbot system. The system comprises a plurality of modules including a key information extraction module embodying a first trained neural network, table detection module embodying a second trained neural network and table extraction module representing a third trained neural network.) See FIG. 4 & Paragraph [0116], (FIG. 4 illustrates chatbot system 400 configured to extract and output ley information and tables from a document using a set of machine-learning models wherein key information extraction module 410 corresponds to a first trained ML model and table extraction module 430 represents a third trained ML model, the outputs of both models are collected as extraction results 450, i.e. individual machine-learning models of the target model bundle are trained to extract unique signal types from the text data related to the one or more interactions;)
generating, by the one or more processors, a signal data object by applying the interaction data object to the target model bundle, wherein: the individual machine-learning models of the target model bundle are separately applied to the interaction data object to generate respective outputs; See Paragraph [0130], (An entity may request to view or inspect a document, causing the processing system to extract text and tables from the document as extraction results which may be output for display to the user. Extraction results may include a combination of outputs from the plurality of machine learning models including text from key fields output by a first trained model and table data output by the third trained model, i.e. generating, by the one or more processors, a signal data object (e.g. generating extraction results) by applying the interaction data object to the target model bundle (e.g. a user request is used to inform the generation of extraction results), wherein: the individual machine-learning models of the target model bundle are separately applied to the interaction data object to generate respective outputs (e.g. the trained machine learning models generate outputs that are collected into extraction results);)
and the generated signal data object includes, in one or more of a relational or tabular format, a combination of the outputs of the plurality of machine-learning models of the target model bundle, the combination being indicative of a particular signal; See Paragraph [0121], (Table extraction module 430 can extract tables from an input document and may output tables as another subset of extraction results 450 which includes output tables, text extracted using key information extraction module 410 and other suitable extraction results.) See Paragraph [0130] , (Data processing system may output extraction results for a document obtained from the plurality of trained machine-learning models and may include text form key fields, tables, other suitable extraction results or a combination thereof, i.e. and the generated signal data object includes, in one or more of a relational or tabular format (e.g. extraction results include tabular data and other information related to the tabular data as output of the at least first, second and third machine learning models), a combination of the outputs of the plurality of machine-learning models of the target model bundle (extraction results comprise a combination of outputs from multiple machine learning models), the combination being indicative of a particular signal (e.g. Note [0130] wherein extraction results generate text, tables and other outputs relating to a request associated with a document);)
and modifying, by the one or more processors, a curated data object by changing one or more data entries based on (i) the generated signal object or (ii) the generated signal object and one or more of the generated intent data object or the generated subject data object. See Paragraph [0069], (A skill bot may perform tasks for a user in response to an input based on the intent inferred from the user input. For example, a skill bot for a bank may be trained to perform actions including "CheckBalance", "TransferMoney", "DepositCheck", etc.) See Paragraphs [0100]-[0101], (Explicit invocation subsystem (EIS) 230 may analyze a user input and/or extracted information 205 to invoke a skill bot for executing the user request, i.e. modifying, by the one or more processors, a curated data object by changing one or more data entries based on (i) the generated signal object (e.g. by invoking a skill bot to execute actions such as acting upon a bank account. An action such as a deposit, withdrawal or transfer would modify attributes of a bank account. EIS uses an extraction result to invoke a skill bot to perform the user request.).)
Jiao, TREHAN and Dharmasiri are analogous art because they are in the same field of endeavor, document processing via machine learning tools. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Jiao-TREHAN to include the method of generating extraction results using outputs from multiple machine learning models as disclosed by Dharmasiri. Paragraph [0115] of Dharmasiri discloses that the use of machine learning models for extracting important or otherwise key information or tables from a document allow the system to hone in on important sections of a document in order to identify the correct information.
Regarding dependent claim 3,
As discussed above with claim 1, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
Jiao further discloses the method further comprising initiating, by the one or more processors, an action based on the modified curated data object. See Paragraph [0193], (User categorization system 1102 may be used to verify or refine categorizations of a user based on the user's knowledge of assertions as determined from answering questions. The system may then make predictions about the user's attributes and categorize the user on that basis.) See Paragraph [0126], (User lifestyle information may be used by recommendation engine 780 to generate recommendations 785 which may include content that suits the user profile parameters, i.e. initiating, by the one or more processors, an action based on the modified curated data object (e.g. a user profile's lifestyle categorizations may be confirmed or refined, leading the system to generate recommendations based on the state of their profile).)
Regarding dependent claim 4,
As discussed above with claim 3, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
Jiao further discloses the step wherein initiating the action includes one or more of: applying the modified curated data object to one or more scoring models, generating one or more metrics related to the subject of the one or more interactions, or generating one or more interventions related to the subject of the one or more interactions. See Paragraph [0126], (User lifestyle information may be used by recommendation engine 780 to generate recommendations 785 which may include content that suits the user profile parameters, i.e. generating one or more interventions related to the subject of the one or more interactions (e.g. the recommendations are generated according to user-specific actions which includes user assertions that allow the system to categorize a user profile).)
Regarding dependent claim 5,
As discussed above with claim 1, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
Jiao further discloses the method further comprising, prior to modifying the curated data object, associating, by the one or more processors, the extracted one or more signals with a user and identifying that one or more signals are indicative of a current need, See Paragraph [0193], (Lifestyle categorizations 1124 may be used to verify or refine categorizations of a user based on the user's knowledge or assertions.) See Paragraph [0126], (Recommendation engine 780 may use information about the user's knowledge in order to generate recommendations 785 which include content that communicates to the user specific actions, lifestyle choices or areas of growth, i.e. prior to modifying the curated data object, associating, by the one or more processors, the extracted one or more signals with a user and identifying that one or more signals are indicative of a current need (e.g. the system may assign categories to users based on user assertions. Recommendations are generated based on user lifestyle categories and are therefore generated after category data is associated with a user).)
wherein the modifying of the curated data object occurs upon determination that the one or more signals are indicative of a current need. See Paragraph [0130], (Recommendation logic 790 may generate and deliver recommendations to a user based on scores associated with individual topics such that the recommendation provided to the user may assist the user's knowledge or lifestyle. An example is provided where a user score in the topics of nutrition or sleep are low. The recommendation provided to the user may identify recommended hours of sleep for the user to suit their lifestyle and lifestyle goals. Note [0193] wherein lifestyle categorizations may be used to verify or refine categorizations of a user based on the user's knowledge or assertions, i.e. modifying of the curated data object occurs upon determination that the one or more signals are indicative of a current need (e.g. user categorizations, which determine the types of recommendations the system generates for them, may be refined over time to reflect changes in user lifestyle and therefore user needs).)
Regarding dependent claim 9,
As discussed above with claim 1, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
Jiao further discloses the step wherein the interaction data object is received from a user interface, See Paragraph [0028], (The system may organize a library of health assertions and/or questions associated with one or more topics. The topics may be utilized to select assertions or questions that may be presented to a user for them to answer such as via a trivia session, i.e. wherein the interaction data object is received from a user interface.)
and the method further comprises displaying the modified curated data object on the user interface, wherein the displayed modified curated data object includes one or more recommended actions based on the generated signal data object. See Paragraph [0145], (FIG. 8E illustrates a panel 840 comprising a menu of options that may be configured to display recommendations, i.e. displaying the modified curated data object on the user interface, wherein the displayed modified curated data object (e.g. recommendations are delivered to a user according to user categorization which may be refined as described in [0193], thereby modifying the recommendations generated for a user) includes one or more recommended actions based on the generated signal data object.)
Regarding independent claim 11,
The claim is analogous to the subject matter of independent claim 1 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 12,
As discussed above with claim 11, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
TREHAN further discloses the step wherein each machine-learning model of the plurality of machine-learning models of the target model bundle is trained to extract a unique signal type from the text data related to the one or more interactions. See Paragraph [0032], (Each of the pre-stored sets of intent maps are generated from a single predefined training input based on an iterative and elastic stretching process such that the pre-stored sets of intent maps may be gradually manipulated and stretched via intent map transforming algorithms. Note [0033] wherein the sets of pre-stored intent maps are matched to a user input in order to render the predefined response mapped to a particular pre-stored intent map. For example, a predetermined response may be delivered to a user based on the user's intent and context, i.e. wherein each machine-learning model of the plurality of machine-learning models of the target model bundle (e.g. intent maps are trained machine learning models that are selected to generate an appropriate output based on a user input and context ) is trained to extract a unique signal type from the text data related to the one or more interactions (e.g. a user input text and context represent user interactions with the system).)
Regarding dependent claim 13,
As discussed above with claim 11, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
Jiao further discloses the step wherein the system is configured to initiating an action based on the modified curated data object, See Paragraph [0193], (User categorization system 1102 may be used to verify or refine categorizations of a user based on the user's knowledge of assertions as determined from answering questions. The system may then make predictions about the user's attributes an categorize the user on that basis.) See Paragraph [0126], (User lifestyle information may be used by recommendation engine 780 to generate recommendations 785 which may include content that suits the user profile parameters, i.e. initiating, by the one or more processors, an action based on the modified curated data object (e.g. a user profile's lifestyle categorizations may be confirmed or refined, leading the system to generate recommendations based on the state of their profile).)
wherein initiating the action includes one or more of: applying the modified curated data object to one or more scoring models, generating one or more metrics related to the subject of the one or more interactions, or generating one or more interventions related to the subject of the one or more interactions. See Paragraph [0126], (User lifestyle information may be used by recommendation engine 780 to generate recommendations 785 which may include content that suits the user profile parameters, i.e. generating one or more interventions related to the subject of the one or more interactions (e.g. the recommendations are generated according to user-specific actions which includes user assertions that allow the system to categorize a user profile).)
Regarding dependent claim 14,
The claim is analogous to the subject matter of dependent claim 5 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 18,
The claim is analogous to the subject matter of dependent claim 9 directed to a computer system and is rejected under similar rationale.
Regarding independent claim 20,
The claim is analogous to the subject matter of independent claim 1 directed to a non-transitory, computer readable medium and is rejected under similar rationale.
Claim(s) 6-7, 10, 15-16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiao in view of TREHAN and Dharmasiri as applied to claim 1 above, and further in view of SABHARWAL et al. (US PGPUB No. 2022/0300716; Pub. Date: Sep. 22, 2022).
Regarding dependent claim 6,
As discussed above with claim 1, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
Jiao-TREHAN-Dharmasiri dos not disclose the step wherein the intent classification machine-learning model is trained to identify associations between text data and corresponding high level intent indicators and granular intent indicators.
SABHARWAL disclose the step wherein the intent classification machine-learning model is trained to identify associations between text data and corresponding high level intent indicators and granular intent indicators. See Paragraph [0061], (Disclosing a system for determining a conversation system from a multi-conversation system using Artificial Intelligence (AI). The AI-based hierarchical multi-conversation system 102 is configured to create a hierarchical tree 300A having child nodes and leaf child nodes using a first pre-trained machine learning model to identify hierarchy for each topic or sub-topic associated with a user query.) See FIG. 3B & Paragraph [0062], (An example is provided wherein a hierarchy is determined for a list of topics such that the system may determine that "Deep learning" is a sub-topic of "machine learning" as illustrated in FIG. 3B
Jiao, TREHAN, Dharmasiri and SABHARWAL are analogous art because they are in the same field of endeavor, text processing via machine learning. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Jiao-TREHAN-Dharmasiri to include the method of training a machine learning module to identify topic-to-sub-topic hierarchies in user query concepts as disclosed by SABHARWAL. Paragraph [0057] of SABHARWAL discloses that the machine learning module 216 may facilitate processing performance improvement of the AI-based hierarchical multi-conversation system 102 by adjusting the processing of input data to result in the desired output data.
Regarding dependent claim 7,
As discussed above with claim 1, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
Jiao-TREHAN-Dharmasiri does not disclose the step wherein the subject classification machine-learning model is trained to identify associations between text data and corresponding subject indicators.
SABHARWAL discloses the step wherein the subject classification machine-learning model is trained to identify associations between text data and corresponding subject indicators. See Paragraph [0061], (Disclosing a system for determining a conversation system from a multi-conversation system using Artificial Intelligence (AI). The AI-based hierarchical multi-conversation system 102 is configured to create a hierarchical tree 300A having child nodes and leaf child nodes using a first pre-trained machine learning model to identify hierarchy for each topic or sub-topic associated with a user query.) See FIG. 3B & Paragraph [0063], (FIG. 3B illustrates the hierarchy of terms including root node 302 where the user query, i.e. text data, is classified into a topic, a domain, a class or a category, i.e. subject indicators.)
Jiao, TREHAN, Dharmasiri and SABHARWAL are analogous art because they are in the same field of endeavor, text processing via machine learning. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Jiao-TREHAN-Dharmasiri to include the method of training a machine learning module to identify topic-to-sub-topic hierarchies in user query concepts as disclosed by SABHARWAL. Paragraph [0057] of SABHARWAL discloses that the machine learning module 216 may facilitate processing performance improvement of the AI-based hierarchical multi-conversation system 102 by adjusting the processing of input data to result in the desired output data.
Regarding dependent claim 10,
As discussed above with claim 1, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
Jiao-TREHAN-Dharmasiri dos not disclose the method further comprising updating, by the one or more processors, one or more of the trained intent classification machine-learning model, the trained subject classification machine-learning model, or at least one of the plurality of machine-learning models within the target model bundle based on feedback received regarding an accuracy of the generated intent data object, the generated subject data object, and the generated signal data object, respectively.
SABHARWAL discloses the method further comprising updating, by the one or more processors, one or more of the trained intent classification machine-learning model, the trained subject classification machine-learning model, or at least one of the plurality of machine-learning models within the target model bundle based on feedback received regarding an accuracy of the generated intent data object, the generated subject data object, and the generated signal data object, respectively. See Paragraph [0038], (Conversation system 102 may not recognize a query response to a received user query and may be configured to identify an answer to previously unrecognized user query and may store the answer for future query response purposes and re-train a conversation system of the AI-based hierarchical multi-conversation system 102 to answer such queries in the future, i.e. updating, by the one or more processors, one or more of the trained intent classification machine-learning model (e.g. updating the graph structure to include previously unknown or inaccurate relationships between topics and sub-topics of a query, i.e. intents, based on feedback received regarding an accuracy of the generated intent data object (e.g. a user or subject matter expert may provide feedback to correct the error).)
Jiao, TREHAN, Dharmasiri and SABHARWAL are analogous art because they are in the same field of endeavor, text processing via machine learning. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Jiao-TREHAN-Dharmasiri to include the method of training a machine learning module to identify topic-to-sub-topic hierarchies in user query concepts as disclosed by SABHARWAL. Paragraph [0057] of SABHARWAL discloses that the machine learning module 216 may facilitate processing performance improvement of the AI-based hierarchical multi-conversation system 102 by adjusting the processing of input data to result in the desired output data.
Regarding dependent claim 15,
The claim is analogous to the subject matter of dependent claim 6 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 16,
The claim is analogous to the subject matter of dependent claim 7 directed to a computer system and is rejected under similar rationale.
Regarding dependent claim 19,
The claim is analogous to the subject matter of dependent claim 10 directed to a computer system and is rejected under similar rationale.
Claim(s) 8 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jiao in view of TREHAN and Dharmasiri as applied to claim 1 above, and further in view of LIU et al. (US PGPUB No. 2021/0286934; Pub. Date: Sep. 16, 2021).
Regarding dependent claim 8,
As discussed above with claim 1, Jiao-TREHAN-Dharmasiri discloses all of the limitations.
Jiao further discloses the step wherein the granular intent indicator represents a hierarchical subtopic of the high level intent indicator, See FIG. 3 & See Paragraph [0094], (The system may generate a data structure that can be developed to link a question with a health outcome (e.g. a granular intent) and a topic (e.g. a high-level intent), i.e. wherein the granular intent indicator represents a hierarchical subtopic of the high level intent indicator (e.g. the health outcomes represent information relevant to the topic).)
Jiao-TREHAN-Dharmasiri does not disclose the step wherein the target model bundle is selected based on the hierarchical subtopic represented by the granular intent indicator.
LIU discloses the step wherein the target model bundle is selected based on the hierarchical subtopic represented by the granular intent indicator. See Paragraph [0057], (Target input text 141 associated with a target text generation task is applied as the input of the task-specific text generation model 140. The task-specific text generation mode may generate a target output text 142 that meets the specific target output attributes. Note [0055] wherein a target text generation task corresponds to text indicating a specific action to be performed by the system, i.e. wherein the target model bundle is selected based on the hierarchical subtopic represented by the granular intent indicator (the fine-tuned model is selected according to the text contents of the target input text, i.e. a granular intent indicator).)
Jiao, TREHAN, Dharmasiri and LIU are analogous art because they are in the same field of endeavor, text processing via machine learning. It would have been obvious to anyone having ordinary skill in the art before the effective filing date to modify the system of Jiao-TREHAN-Dharmasiri to include the method of generating and selecting a task-specific text generation model for processing an input text and generating an output as disclosed by LIU. Paragraph [0029] of LIU discloses that the use of a task-specific text generation model that is generated for each specific task encountered by the system represents an improved text generation solution that is configured to handle any of a plurality of text-related tasks by fine-tuning a base model.
Regarding dependent claim 17,
The claim is analogous to the subject matter of dependent claim 8 directed to a computer system and is rejected under similar rationale.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 11 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments necessitated the new grounds of rejection presented in this Office Action.
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 Fernando M Mari whose telephone number is (571)272-2498. The examiner can normally be reached Monday-Friday 7am-4pm.
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/FMMV/Examiner, Art Unit 2159 /AMRESH SINGH/Primary Examiner, Art Unit 2159