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 .
This action is in response to the communication filed on June 05, 2024.
Claims 1-20 are examined and are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 05, 2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
Claims 10 -11, and 13 has limitation “data segregator is configured to receive …, segregate …”, “native accumulator configured to receive ..., perform …”, “the autodidact engine configured to provide …”, has been interpreted under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, because it uses a non-structural term “receive, segregate, perform” coupled with functional language “configured to” without reciting sufficient structure to achieve the function. Furthermore, the non-structural term is not preceded by a structural modifier.
Since this claim limitation invokes 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, claims 10-11 and 13 are interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph limitation: figure 10 and specification Para [0087].
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not wish to have the claim limitation treated under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, applicant may amend the claim so that it will clearly not invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, or present a sufficient showing that the claim recites sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance with 35 U.S.C. § 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Claims 1-2, 5-11 and 14-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Raghunandan (US 6,760,722 B1), in view of Wu et al (US 2022/0398271 A1).
As per claim 1, Raghunandan discloses:
- a method for providing aid to the industrial workforce based on intelligent learning, comprising (column 2, line 40-45, column 4, line 12-15, a method for automatically processing support request (i.e., aid to the industrial workforce) based on self-learning (i.e., intelligent learning)),
- receiving a query via an input acceptor (column 3, line 43-50, Fi. 1, item 1, Fig. 2, item 1, receiving requests using an input),
- routing the query to an autodidact engine, said autodidact engine comprising a knowledge base and a native accumulator (Fig. 2, item 6-9, column 2, line 1-5, 15-20, 45-50, 55-65, forwarding the request (i.e., routing the query) to database consisting product information and knowledge base (i.e., autodidact engine with knowledge base and native accumulator)),
- receiving, by a data segregator, data associated with a product or services from one or more data sources, said data sources including at least a product data source, a customer data source and a tribal knowledge source (Fig. 2, item 6-9, column 2, line 15-20, 45-65, multiproduct view cache, knowledge cache with various product information (i.e., data sources with product information) and knowledge base (i.e. tribal knowledge source)),
- and providing the responses to the customer corresponding to the query received through the user input acceptor (column 3, line 30-40, column 8, line 55-60, response to the request corresponding to the user request),
Raghunandan does not explicitly discloses segregating, by the data segregator, the received data from said data sources into structured and unstructured data; providing, by the data segregator, the structured and the unstructured data to the native accumulator; performing, by the native accumulator, one or more operations to process the received data from the segregator; storing the processed data from the native accumulator into the knowledge base; executing a deep learning framework to process the data from the native accumulator; and providing the responses to the customer corresponding to the query received through the user input acceptor. However, in the same field of endeavor Wu in an analogous art discloses segregating, by the data segregator, the received data from said data sources into structured and unstructured data (Fig. 3, item 300, Para [0002], [0033], extracted data (i.e., received data) separated based on structured ana unstructured data), providing, by the data segregator, the structured and the unstructured data to the native accumulator (Para [0008], [0025] – [0026], extracting facts from the data and provided to natural language processing (i.e., providing structured and unstructured data to native accumulator)), performing, by the native accumulator, one or more operations to process the received data from the segregator (Fig. 4, item 410, 412, , 414, Para [0045] – [0047], normalizing and checking inconsistency (i.e., operation to process received data)), storing the processed data from the native accumulator into the knowledge base (Fig. 9, item 914, [0004], Para [0026], [0027], adding the fact to the knowledge graph to make it available for later querying (i.e., storing processed data to the knowledge data)), executing a deep learning framework to process the data from the native accumulator (Fig. 5, layer 2, Para [0025], [0048], [0058], deep learning model to process the data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate segregating structured and unstructured data and processing such data using deep learning as taught by Wu as the means to response to a support query using data from native accumulator and knowledge base in Raghunandan, (Raghunandan, column 2, line 40-45, column 4, line 12-15, Wu, Para [0002], [0033]). Raghunandan and Wu are analogous prior art since they both deal with receiving user query to solve a particular issue or problem using native data and knowledge base data. A person of the ordinary skill in the art would have been motivated to make aforementioned modification to improve the effectiveness of searching, (Raghunandan, column 2, line 10-15).
As per claim 2, rejection of claim 1 is incorporated and further Raghunandan discloses:
- receiving a feedback from one or more customer through the input acceptor corresponding to the responses provided by the autodidact engine (column 3, line 25-30, proving suggestion and follow-up questions (i.e., feedback) from the customer).
As per claim 5, rejection of claim 1 is incorporated, and further Wu disclsoes:
- wherein the query received from the customer though the input acceptor includes audio, video or text-based query (Para [0006], [0071], received query is a text and utter or audible based query).
As per claim 6, rejection of claim 1 is incorporated, and further Wu discloses:
- processing the received structured and unstructured data from data sources by applying natural language processing techniques and industrial vernacular libraries (Para [0025], processing data by applying natural language and ontology (i.e., industrial library)).
As per claim 7, rejection of claim 1 is incorporated, and further Raghunandan discloses:
- updating the knowledge base based on the data processed by the native accumulator, the data being accumulated from one or more sources including the product data source, the customer data source and the tribal knowledge source (Fig. 3, column 3, line 30-40, column 4, line 30-40, column 6, line 10-25, updating knowledge base based on any whenever the expert system identifies the occurrence of new behavior, or when a new product or a revision to an existing product are released into the system or when a solution is provided by the system expert to a problem that was forwarded by the automatic remote support system).,
As per claim 8, rejection of claim 1 is incorporated, and further Raghunandan discloses:
- wherein the data collected from product data source, the customer data source and the tribal knowledge source includes the data pertaining to an industrial product or an industrial process (colu4, line 60-65, collected data source includes product containing information related to function, specification, etc.).
As per claim 9, rejection of claim 8 is incorporated, and further Raghunandan discloses:
- wherein the industrial product data comprises at least one or more of product performance, product health index, product degradation index, remaining useful life of product and the industrial process data comprises at least one or more of monitoring process performance, determining process anomaly and predicting the life of the product (column 2, line 55-60, column 6, line 10-25, column 8, line 25-45, monitoring the behavior of a particular product (i.e., performance of the product or health of the product)).
Claims 3 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Raghunandan (US 6,760,722 B1), in view of Wu et al (US 2022/0398271 A1), as applied to claim 1 and 10 above, and further in view of Meenavalli et al (US 2023/0230063 A1).
As per claim 3, rejection of claim 2 is incorporated,
Combined method of Raghunandan and Wu does not explicitly disclose training the deep learning framework based on the feedback received from one or more customers corresponding to the responses provided by the autodidact engine. However, in the same field of endeavor Meenavalli in an analogous art disclose training the deep learning framework based on the feedback received from one or more customers corresponding to the responses provided by the autodidact engine (Abstract, line 8-10, Para [0005], training deep learning model based on user feedback).
Therefore, it would have been obvious to a person of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Raghunandan, as previously modified with Wu, with the teaching of Meenavalli by modifying Raghunandan such that deep learning method is trained to retrieve correct answer for a user query related to resolution. The motivation for doing so would be training and development environment seamlessly interacts with the execution environment to iteratively improve the models over time as new situations arise or as the production error dataset grows, (Meenavilla, Para [0049]).
As per claim 12,
claim 12 is the system claim corresponding to method claim 3 respectively, and rejected under the same reason set forth to the rejection of claim 3 above.
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Raghunandan (US 6,760,722 B1), in view of Wu et al (US 2022/0398271 A1), as applied to claim 1 and 10 above, and further in view of Jensen et al (US 2025/0200528 A1).
As per claim 4, rejection of claim 1 is incorporated,
Combined method of Raghunandan and Wu does not explicitly disclose providing autodidact engine in online or offline mode, which is accessible by one or more customers through the input acceptor. However, in the same field of endeavor Jensen in an analogous art disclose providing autodidact engine in online or offline mode, which is accessible by one or more customers through the input acceptor (Fig. 2, item 218, 220, Para [0040], cloud - based system (i.e., autodidact engine) in online or offline mode).
Therefore, it would have been obvious to a person of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Raghunandan, as previously modified with Wu, with the teaching of Jensen by modifying Raghunandan such that machine learning to provide suggestions on which tests to perform to diagnose the issue and/or which repair steps to perform to fix the issue during online or offline. The motivation for doing so would be customizing users troubleshooting workflow and learn from multiple users' actions and experiences, and based on the learning, the system can recommend the most relevant test and/or repair efficiently, (Jensen Para [0002]).
As per claim 12,
claim 12 is the system claim corresponding to method claim 4 respectively, and rejected under the same reason set forth to the rejection of claim 12 above.
As per claim 10, Raghunandan discloses:
- a system for providing aid to the industrial workforce based on intelligent learning, comprising (column 2, line 40-45, column 4, line 12-15, a method for automatically processing support request (i.e., aid to the industrial workforce) based on self-learning (i.e., intelligent learning)),
- an input acceptor for receiving a query from a customer (column 3, line 43-50, Fi. 1, item 1, Fig. 2, item 1, receiving requests using an input),
- an autodidact engine coupled to the input acceptor for receiving the query, said autodidact engine comprising a knowledge base, a native accumulator and a deep learning framework (Fig. 2, item 1 (input acceptor), item connected to item 5 and 7 and 9 (autodidact engine) connected to item 8 knowledge base and self-learning/machine learning system (i.e., deep learning system), column 2, line 35-55),
- receive data from one or more sources, said sources including product data, customer data and tribal knowledge data (Fig. 2, item 6-9, column 2, line 15-20, 45-65, multiproduct view cache, knowledge cache with various product information (i.e., data sources with product information) and knowledge base (i.e. tribal knowledge source)),
- the deep learning framework coupled to the native accumulator and is configured to process the data to provide the responses to the customer corresponding to the query through the input acceptor (column 2, line 20-30, 40-55, self-learning and machine learning (i.e., deep learning) couple to knowledge base and caches (Fig. 2, item 6-9), column 3, line 30-40, column 8, line 55-60, response to the request corresponding to the user request),
Raghunandan does not explicitly discloses a data segregator coupled to the native accumulator; wherein the data segregator is configured to: segregate the received data into structure data and unstructured data; and provide the structure data and unstructured data to the native accumulator; wherein the native accumulator is configured to: receive the structure data and unstructured data from the data segregator; performing one or more operations to process the received data, said operation including natural language processing techniques; storing the processed data into the knowledge base; and provide the processed data to the deep learning framework. However, in the same field of endeavor Wu in an analogous art discloses a data segregator coupled to the native accumulator (Fig. 3, item 300, Para [0002], [0033], extracted data (i.e., received data) separated based on structured ana unstructured data), wherein the data segregator is configured to: segregate the received data into structure data and unstructured data and provide the structure data and unstructured data to the native accumulator; wherein the native accumulator is configured to: receive the structure data and unstructured data from the data segregator Para [0008], [0025] – [0026], extracting facts from the data and provided to natural language processing (i.e., providing structured and unstructured data to native accumulator)), performing one or more operations to process the received data, said operation including natural language processing techniques Fig. 4, item 410, 412, , 414, Para [0045] – [0047], normalizing and checking inconsistency (i.e., operation to process received data)), storing the processed data into the knowledge base (Fig. 9, item 914, [0004], Para [0026], [0027], adding the fact to the knowledge graph to make it available for later querying (i.e., storing processed data to the knowledge data))and provide the processed data to the deep learning framework (Fig. 5, layer 2, Para [0025], [0048], [0058], deep learning model to process the data).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to incorporate segregating structured and unstructured data and processing such data using deep learning as taught by Wu as the means to response to a support query using data from native accumulator and knowledge base in Raghunandan, (Raghunandan, column 2, line 40-45, column 4, line 12-15, Wu, Para [0002], [0033]). Raghunandan and Wu are analogous prior art since they both deal with receiving user query to solve a particular issue or problem using native data and knowledge base data. A person of the ordinary skill in the art would have been motivated to make aforementioned modification to improve the effectiveness of searching, (Raghunandan, column 2, line 10-15).
Claims 1-2, 5-11 and 14-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Raghunandan (US 6,760,722 B1), in view of Wu et al (US 2022/0398271 A1), and further in view of Jensen et al (US 2025/0200528 A1).
As per claim 19, rejection of claim 10 is incorporated, and further Raghunandan discloses:
- the knowledge base comprises various static and dynamic data covering the product, users document, management system (column 2, line 55-60, new product (i.e., dynamic data) and existing product (i.e., static data)),
- the product data source comprises data from catalog, product manuals, user documentations (column 4, line 60-65, specification of a product (i.e., catalog, product manual)),
- and the tribal knowledge source comprises data and knowledge acquired by expertise and product experts (column 2, line 25-35, column 3, line 30-50, knowledge with experise).
Combined method of Raghunandan and Wu does not explicitly disclose the customer data source comprises data from logbook, system operating procedure, maintenance procedures, manuals. However, in the same field of endeavor Jensen discloses the customer data source comprises data from logbook, system operating procedure, maintenance procedures, manuals (Para [0001], owner’s manual, Para [0147], log book, Para [0035], trouble shooting procedure),
Therefore, it would have been obvious to a person of the ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Raghunandan, as previously modified with Wu, with the teaching of Jensen by modifying Raghunandan such that machine learning to provide suggestions on which tests to perform to diagnose the issue and/or which repair steps to perform to fix the issue during online or offline. The motivation for doing so would be customizing users troubleshooting workflow and learn from multiple users' actions and experiences, and based on the learning, the system can recommend the most relevant test and/or repair efficiently, (Jensen Para [0002]).
As per claims 11 and 14-18,
Claims 11 and 14-18 are system claims corresponding to method claims 2 and 5-9 respectively, and rejected under the same reason set forth to the rejection of claims 2 and 5-9 above.
As per claim 20,
Claim 20 is a computer readable medium claim corresponding to method claims 1 respectively, and rejected under the same reason set forth to the rejection of claim 1 above.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED R UDDIN whose telephone number is (571)270-3138. The examiner can normally be reached M-F: 9:00 AM-5:00 PM.
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/MOHAMMED R UDDIN/Primary Examiner, Art Unit 2161
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