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 .
Detailed Action
2. Claims 1-25 are pending in Instant Application.
Response to Arguments
Applicant’s arguments with respect to independent claims 1, 8, and 12 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.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-25 are rejected under 35 U.S.C. 103 as being unpatentable over WO 2024/213993A1
issued to Abilash et al. (Abilash) in view of US 2024/0370769 issued to Sheth et al. (Sheth) and further in view of WO 2023/182989A1 issued to Rahul et al. (Rahul).
As per claim 1, Abilash teaches a method, comprising: classifying, by a governance framework, a user query into one or more constituent components (Abilash: Pg. 3, ll. (6-10) - the query receiving module is configured to receive a plurality of queries from the user with the unified identity and classify the plurality of queries into one or more categories for providing a response output. The received plurality of queries are multimodal queries. The governance module is operatively coupled to the query receiving module); mapping, by the governance framework, the one or more constituent components to one or more different modalities (Abilash: Pg 3, ll. (10-12) - the governance module is configured to understand a context of the multimodal queries based on a user requirement for preventing or allowing input of the multimodal query);
Abilash however does not explicitly teach routing, by the governance framework, the one or more constituent components to one or more modal application specific networks (ASNs) each corresponding to one of the one or more different modalities; combining, by the governance framework, results received from the one or more modal ASNs into a unified response to the user query; and verifying, by the governance framework, the results received from the one or more modal ASNs with one or more compliance rules.
Sheth however explicitly teaches routing, by the governance framework, the one or more constituent components to one or more modal application specific networks (ASNs) each corresponding to one of the one or more different modalities (Sheth: Fig. 1, ¶ 0033 - data processing subsystem 114 may pass the sub-queries to machine learning subsystem 116. Machine learning subsystem 116 may input, into a specialized language model, the plurality of sub-queries to obtain a plurality of accurate responses to the plurality of sub-queries. In some embodiments, the specialized machine learning model may map each sub-query into a vector space of the specialized language model. A specialized model of specialized models 108a-108n may extract specific facts or information from (based on Fig. 1 specialized models 108a-108n are different parts of networks that can be used for different modalities)); combining, by the governance framework, results received from the one or more modal ASNs into a unified response to the user query (Sheth: ¶ 0036 - each individual neural unit may have a summation function which combines the values of all of its inputs together); and verifying, by the governance framework, the results received from the one or more modal ASNs with one or more compliance rules (Sheth: ¶ 0054 - when the response is received, machine learning subsystem 116 may pass the response to sentinel subsystem 118. Sentinel subsystem 118 may scan the response to identify any undesired text or other data within the response and check if the responses conform to policy or not).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the teaching of Abilash in view of Sheth to teach routing, by the governance framework, the one or more constituent components to one or more modal application specific networks (ASNs) each corresponding to one of the one or more different modalities; combining, by the governance framework, results received from the one or more modal ASNs into a unified response to the user query; and verifying, by the governance framework, the results received from the one or more modal ASNs with one or more compliance rules. One would be motivated to do so as a specialized model of specialized models 108a-108n may extract specific facts or information from (specialized models 108a-108n are different parts of networks that can be used for different modalities) and also when the response is received, machine learning subsystem may pass the response to sentinel subsystem. Sentinel subsystem may scan the response to identify any undesired text or other data within the response and check if the responses conform to policy or not (Sheth: Fig. 1, ¶ 0033, ¶ 0036, ¶ 0054).
The modified teaching of Abilash however does not explicitly teach wherein verifying the results comprises cross-feeding the results from a first ASN of the one or more modal ASNs into a second ASN of the one or more modal ASNs.
Rahul however explicitly teaches wherein verifying the results comprises cross-feeding the results from a first ASN of the one or more modal ASNs into a second ASN of the one or more modal ASNs (Rahul: ¶ 0066 - the deep hash composer 720 may also apply a set of cross-modal hash functions to create a pairwise hash composition for cross-modal hash index evaluation operation. The deep hash composer 720 may also select the type of registry a query in the distributed index registry server 740 should target based on a mode and data type associated with the query regarding image 715 and the type of analysis for each type of registry (e.g., identification 731 or a check 732). In some aspects, a verification and/or membership query may include a check 732 for an association between the image 715 (i.e., a first type of captured data) and data captured by other types of sensors in the distributed index registry server 740. For example, a query related to a daytime image may additionally include a check 732 for an association with another type of captured data (e.g., a nighttime image or a point cloud) in a cross-modal registry. The results 745 (e.g., a status or an index) of the identification 731 and the check 732 may then be provided to the client computing device from which the query regarding image 715 was received).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein verifying the results comprises cross-feeding the results from a first ASN of the one or more modal ASNs into a second ASN of the one or more modal ASNs. One would be motivated to do so as the deep hash composer may also apply a set of cross-modal hash functions to create a pairwise hash composition for cross-modal hash index evaluation operation. The deep hash composer may also select the type of registry a query in the distributed index registry server should target based on a mode and data type associated with the query regarding image and the type of analysis for each type of registry. In some aspects, a verification and/or membership query may include a check for an association between the image (i.e., a first type of captured data) and data captured by other types of sensors in the distributed index registry serve. For example, a query related to a daytime image may additionally include a check for an association with another type of captured data (e.g., a nighttime image or a point cloud) in a cross-modal registry. The results (e.g., a status or an index) of the identification and the check may then be provided to the client computing device from which the query regarding image was received (Rahul: ¶ 0066).
As per claim 2, the modified teaching of Abilash in view of Rahul teaches the method of claim 1, further comprising providing, by the governance framework, the unified response to a user (Abilash: Pg. 3, ll. (6-8) - the query receiving module is configured to receive a plurality of queries from the user with the unified identity and classify the plurality of queries into one or more categories for providing a response output).
As per claim 3, the modified teaching of Abilash in view of Rahul teaches the method of claim 1, further comprising: verifying, by the governance framework, the user query for compliance with the one or more compliance rules; and rejecting, by the governance framework, the user query if it fails to comply with at least one of the one or more compliance rules (Sheth: ¶ 0054 - the sentinel machine learning model may be trained to identify undesired components of the response. Undesired responses may be responses that are inaccurate, contain vulgar language, contain wrong or non-existing citations, and/or responses that do not conform to policy).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach verifying, by the governance framework, the user query for compliance with the one or more compliance rules; and rejecting, by the governance framework, the user query if it fails to comply with at least one of the one or more compliance rules. One would be motivated to do so as the sentinel machine learning model may be trained to identify undesired components of the response. Undesired responses may be responses that are inaccurate, contain vulgar language, contain wrong or non-existing citations, and/or responses that do not conform to policy (Sheth: ¶ 0054).
As per claim 4, the modified teaching of Abilash in view of Rahul teaches the method of claim 1, wherein classifying the user query into one or more constituent components comprises providing the user query to one or more artificial neural networks (ANNs) for classification (Sheth: ¶ 0036 - teaches the machine learning model may include an artificial neural network and ¶ 0033 - teaches the machine learning subsystem input the queries and sub-queries).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein classifying the user query into one or more constituent components comprises providing the user query to one or more artificial neural networks (ANNs) for classification. One would be motivated to do so as the machine learning model may include an artificial neural network while the machine learning subsystem input the queries and sub-queries (Sheth: ¶ 0033, ¶ 0036).
As per claim 5, the modified teaching of Abilash in view of Rahul teaches the method of claim 4, wherein the routing of the one or more constituent components to the one or more modal ASNs is based upon the classification from the one or more ANNs (Sheth: ¶ 0036 - each neural unit (neural network) of the machine learning model may be connected to one or more other neural units of the machine learning model).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein the routing of the one or more constituent components to the one or more modal ASNs is based upon the classification from the one or more ANNs. One would be motivated to do so as each neural unit (neural network) of the machine learning model may be connected to one or more other neural units of the machine learning model (Sheth: ¶ 0036).
As per claim 6, the modified teaching of Abilash in view of Rahul teaches the method of 1, further comprising: receiving, from the one or more modal ASNs, corresponding one or more results; analyzing, by the governance framework, the corresponding one or more results to ascertain one or more additional queries; submitting, by the governance framework, the one or more additional queries to one or more of the modal ASNs; and receiving, from the one or more modal ASNs, revised results (Sheth: ¶ 0035 - if the machine learning model is a neural network, to reconcile differences between the neural network's prediction and the reference feedback. One or more neurons of the neural network may require that their respective errors are sent backward through the neural network to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model may be trained to generate better predictions of information sources that are responsive to a query).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach receiving, from the one or more modal ASNs, corresponding one or more results; analyzing, by the governance framework, the corresponding one or more results to ascertain one or more additional queries; submitting, by the governance framework, the one or more additional queries to one or more of the modal ASNs; and receiving, from the one or more modal ASNs, revised results. One would be motivated to do so as if the machine learning model is a neural network, to reconcile differences between the neural network's prediction and the reference feedback. One or more neurons of the neural network may require that their respective errors are sent backward through the neural network to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model may be trained to generate better predictions of information sources that are responsive to a query (Sheth: ¶ 0035).
As per claim 7, the modified teaching of Abilash in view of Rahul teaches the method of claim 6, wherein verifying results received from the one or more modal ASNs with the one or more compliance rules comprises verifying the revised results with the one or more compliance rules (Sheth: ¶ 0054 - sentinel subsystem may input the response into a sentinel machine learning model (e.g., sentinel model) to generate a modified response. The sentinel machine learning model may be trained to identify undesired components of the response. Undesired responses may be responses that are inaccurate, contain vulgar language, contain wrong or non-existing citations, and/or responses that do not conform to policy).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein verifying results received from the one or more modal ASNs with the one or more compliance rules comprises verifying the revised results with the one or more compliance rules. One would be motivated to do so as the sentinel subsystem may input the response into a sentinel machine learning model (e.g., sentinel model) to generate a modified response. The sentinel machine learning model may be trained to identify undesired components of the response. Undesired responses may be responses that are inaccurate, contain vulgar language, contain wrong or non-existing citations, and/or responses that do not conform to policy (Sheth: ¶ 0054).
As per claim 8, the claim resembles claim 1 and is rejected under the same rationale while Abilash teaches non-transitory computer-readable medium (CRM) comprising instructions that, when executed by a processor of an apparatus (Abilash: Pg. 17, ll. (12-15) - Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read-only memory, random access memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, hard drive, removable media drive).
As per claim 9, the modified teaching of Abilash in view of Rahul teaches the CRM of claim 8, wherein the instructions are to further cause the governance framework to cross-check the results of one of the one or more modal ASNs with a consensus of responses from a plurality of the one or more modal ASNs (Sheth: ¶ 0035 - if the machine learning model is a neural network, to reconcile differences between the neural network's prediction and the reference feedback. One or more neurons of the neural network may require that their respective errors are sent backward through the neural network to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model may be trained to generate better predictions of information sources that are responsive to a query).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein the instructions are to further cause the governance framework to cross-check the results of one of the one or more modal ASNs with a consensus of responses from a plurality of the one or more modal ASNs. One would be motivated to do so as if the machine learning model is a neural network, to reconcile differences between the neural network's prediction and the reference feedback. One or more neurons of the neural network may require that their respective errors are sent backward through the neural network to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model may be trained to generate better predictions of information sources that are responsive to a query (Sheth: ¶ 0035).
As per claim 10, the modified teaching of Abilash in view of Rahul teaches the CRM of claim 8, wherein the instructions are to further cause the apparatus to read a configuration from a user configuration file, and adjust operation of the governance framework according to the configuration (Sheth: ¶ 0035 - the machine learning model may update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction (e.g., of an information source), and reference feedback information (e.g., user indication of accuracy, reference labels, or other information)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein the instructions are to further cause the apparatus to read a configuration from a user configuration file, and adjust operation of the governance framework according to the configuration. One would be motivated to do so as the machine learning model may update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction (e.g., of an information source), and reference feedback information (e.g., user indication of accuracy, reference labels, or other information) (Sheth: ¶ 0035).
As per claim 11, the modified teaching of Abilash in view of Rahul teaches the CRM of claim 8, wherein the instructions are to further cause the apparatus to automate one or more operations of the governance framework (Sheth: ¶ 0046 - machine learning subsystem may determine that additional data is available for generating a response. For example, a user or other automated system (e.g., web or internet or database query) may supply an additional file (e.g., text file, audio file, video file, or another suitable file) as part of the original query).
As per claim 12, the claim resembles claim 1 and is rejected under the same rationale.
As per claim 13, the modified teaching of Abilash in view of Rahul teaches the system of claim 12, wherein one or more of the plurality of ASNs are located remote from the governance framework (Sheth: ¶ 0064 - a computer program may be deployed to be executed on one or more computer processors located distributed across multiple remote sites and interconnected by a communication network).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein one or more of the plurality of ASNs are located remote from the governance framework. One would be motivated to do so as a computer program may be deployed to be executed on one or more computer processors located distributed across multiple remote sites and interconnected by a communication network (Sheth: ¶ 0064).
As per claim 14, the modified teaching of Abilash in view of Rahul teaches the system of claim 12, wherein at least one of the plurality of ASNs is implemented as an artificial neural network (ANN) (Sheth: ¶ 0036 - teaches the machine learning model may include an artificial neural network).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein at least one of the plurality of ASNs is implemented as an artificial neural network (ANN). One would be motivated to do so as the machine learning model may include an artificial neural network (Sheth: ¶ 0036).
As per claim 15, the modified teaching of Abilash in view of Rahul teaches the system of claim 14, wherein a subset plurality of the plurality of ASNs are implemented as ANNs that accept a common modality (Sheth: Fig. 1, ¶ 0033 - data processing subsystem 114 may pass the sub-queries to machine learning subsystem 116. Machine learning subsystem 116 may input, into a specialized language model, the plurality of sub-queries to obtain a plurality of accurate responses to the plurality of sub-queries. In some embodiments, the specialized machine learning model may map each sub-query into a vector space of the specialized language model. A specialized model of specialized models 108a-108n may extract specific facts or information from (based on Fig. 1 specialized models 108a-108n are different parts of networks that can be used for different modalities)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein a subset plurality of the plurality of ASNs are implemented as ANNs that accept a common modality. One would be motivated to do so as data processing subsystem may pass the sub-queries to machine learning subsystem. Machine learning subsystem may input, into a specialized language model, the plurality of sub-queries to obtain a plurality of accurate responses to the plurality of sub-queries. In some embodiments, the specialized machine learning model may map each sub-query into a vector space of the specialized language model. A specialized model of specialized models 108a-108n may extract specific facts or information from (based on Fig. 1 specialized models 108a-108n are different parts of networks that can be used for different modalities) (Sheth: ¶ 0036).
As per claim 16, the modified teaching of Abilash in view of Rahul teaches the system of claim 12, wherein one or more of the classifier, mapper, router, and compliance verifier are implemented as ANNs (Sheth: Fig. 1).
As per claim 17, the modified teaching of Abilash in view of Rahul teaches the system of claim 12, wherein at least one of the plurality of ASNs is of a different type from at least another of the plurality of ASNs (Sheth: Fig. 1 (different language models are connected to network)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein at least one of the plurality of ASNs is of a different type from at least another of the plurality of ASNs. One would be motivated to do so as different language models are connected to network (Sheth: Fig. 1).
As per claim 18, the modified teaching of Abilash in view of Rahul teaches the system of claim 12, further comprising a user configuration to modify the functionality of one or more of the components of the governance framework (Abilash: Pg. 24, Claim 6 - the processing subsystem comprises a prompt engineering module operatively coupled to the governance module wherein the prompt engineering module is configured to modify the prompt based on the responsible generative artificial intelligence model to generate the output based on the query or feedback received from a user.
As per claim 19, the modified teaching of Abilash in view of Rahul teaches the system of claim 12, further comprising a metrics and analytics module to collect metrics on the performance of the governance framework (Sheth: ¶ 0059 - the performance of the individual models such as 106, 108a-108n, 110 may be improved using reinforcement learning or other methods based on many instances of human feedback).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach comprising a metrics and analytics module to collect metrics on the performance of the governance framework. One would be motivated to do so as the performance of the individual models may be improved using reinforcement learning or other methods based on many instances of human feedback (Sheth: ¶ 0059).
As per claim 20, the modified teaching of Abilash in view of Rahul teaches the system of claim 12, wherein the classifier is to resolve ambiguities in the user query (Sheth: ¶ 0042 - machine learning subsystem may train the model where the corresponding vectors of the prompts/questions match certain portions (e.g., vectors associated with those portions), while other prompts/questions do not match other portions).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein the classifier is to resolve ambiguities in the user query. One would be motivated to do so as machine learning subsystem may train the model where the corresponding vectors of the prompts/questions match certain portions (e.g., vectors associated with those portions), while other prompts/questions do not match other portions (Sheth: ¶ 0042).
As per claim 21, the modified teaching of Abilash in view of Rahul teaches the system of claim 12, further comprising a global coordinator to coordinate and synchronize actions of the components of the governance framework (Sheth: ¶ 0034 - exemplary machine learning model (global coordinator) may be trained to function as a specialized language model, a large language model, and/or a query building model).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach comprising a global coordinator to coordinate and synchronize actions of the components of the governance framework. One would be motivated to do so as exemplary machine learning model (global coordinator) may be trained to function as a specialized language model, a large language model, and/or a query building model (Sheth: ¶ 0034).
As per claim 22, the modified teaching of Abilash in view of Rahul teaches the system of claim 21, further comprising an automator to perform actions with the components of the governance framework as directed by the global coordinator (Sheth: ¶ 0024 - hallucination prevention system may select a source automatically based on the question/query from the user).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach comprising an automator to perform actions with the components of the governance framework as directed by the global coordinator. One would be motivated to do so as hallucination prevention system may select a source automatically based on the question/query from the user (Sheth: ¶ 0024).
As per claim 23, the modified teaching of Abilash in view of Rahul teaches the system of claim 19, further comprising an automator and a global coordinator, wherein the automator and global coordinator are to adjust operations of the governance framework in response to the collected metrics to achieve a desired performance (Sheth: ¶ 0035 - connection weights may be adjusted, for example, if the machine learning model is a neural network, to reconcile differences between the neural network's prediction and the reference feedback. One or more neurons of the neural network may require that their respective errors are sent backward through the neural network to facilitate the update process (e.g., backpropagation of error)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach comprising an automator and a global coordinator, wherein the automator and global coordinator are to adjust operations of the governance framework in response to the collected metrics to achieve a desired performance. One would be motivated to do so as connection weights may be adjusted, for example, if the machine learning model is a neural network, to reconcile differences between the neural network's prediction and the reference feedback. One or more neurons of the neural network may require that their respective errors are sent backward through the neural network to facilitate the update process (e.g., backpropagation of error) (Sheth: ¶ 0035).
As per claim 24, the modified teaching of Abilash in view of Rahul teaches the system of claim 23, wherein the desired performance is specified in a user configuration (Sheth: ¶ 0035 - the machine learning model may update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction (e.g., of an information source), and reference feedback information (e.g., user indication of accuracy, reference labels, or other information)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein the desired performance is specified in a user configuration. One would be motivated to do so as the machine learning model may update its configurations (e.g., weights, biases, or other parameters) based on the assessment of its prediction (e.g., of an information source), and reference feedback information (e.g., user indication of accuracy, reference labels, or other information) (Sheth: ¶ 0035).
As per claim 25, the modified teaching of Abilash in view of Rahul teaches the system of claim 23, wherein to adjust operations of the governance framework comprises adjusting parameters of one or more of the components of the governance framework (Sheth: ¶ 0035 - the output parameters may be fed back to the machine learning model as input (adjusted) to train the machine learning model (e.g., alone or in conjunction with user indications of the accuracy of outputs, labels associated with the inputs, or with other reference feedback information)).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the modified teaching of Abilash in view of Rahul to teach wherein to adjust operations of the governance framework comprises adjusting parameters of one or more of the components of the governance framework. One would be motivated to do so as the output parameters may be fed back to the machine learning model as input (adjusted) to train the machine learning model (e.g., alone or in conjunction with user indications of the accuracy of outputs, labels associated with the inputs, or with other reference feedback information) (Sheth: ¶ 0035).
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SM AZIZUR RAHMAN whose telephone number is (571) 270-7360. The examiner can normally be reached on M-F Telework;
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ali Shayanfar can be reached on 571-270-1050. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SM A RAHMAN/Primary Examiner, Art Unit 2434
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