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
This office action is in response to application 18/976,715, which was filed 12/11/24. Claims 1-20 are pending in the application and have been considered.
Claim Rejections - 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-3, 7, 9-11, 13-16, 18, and 19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kumar et al. (US 10963636).
Consider claim 1, Kumar discloses a computer-implemented method (methods implemented by data processors of a computing system, Col 2 lines 1-4) comprising:
automatically identifying a free-form text message received by a user of a first organization (receiving free form text from a user, e.g. “Start a RFI on 30-th April and let it run till 30th October. Price will be in INR” and automatically classifying event type, dates, currency, etc., Col 5 lines 10-15, Fig. 2, Col 2-3 lines 59-2);
automatically determining, using a first trained artificial intelligence model, that the free-form text message corresponds to a request to create a sourcing event for a sourcing application (parsing the message and identifying type of event as RFP, RFI, etc., Col 2-3 lines 59-2, which is interpreted as a request to create a new sourcing object event as part of a supply chain procurement process, Col 2 lines 42-48; the parsing using a pre-trained language model, Col 3 lines 29-37);
determining to automatically create the sourcing event in the sourcing application (based on the parsed user input, selecting an appropriate template to create a new sourcing object event, Col 2 lines 42-48, Col 2-3 lines 59-10);
automatically extracting, from the free-form text message and using a second trained artificial intelligence model, sourcing event fields for the sourcing event (the parsed user-generated input is then used by a second machine learning model to determine a suggested template comprising a plurality of fields for initiating the event, Col 5 lines 32-48, Fig 4, the fields prepopulated based on the user input, Col 3 lines 11-13);
automatically determining, using a third trained artificial intelligence model, a sourcing event template for the sourcing event (machine learning model trained to select an optimal template for the sourcing event, Col 4-5 lines 52-9, the machine learning model that populates at least a portion of the fields being a different one than the one that selects the suggested template, Col 42-45; the model that suggests the template is therefore considered “a third trained artificial intelligence model”); and
automatically invoking an interface of the sourcing application to create the sourcing event in the sourcing application, including providing, to the interface, sourcing event fields extracted from the free-form text message and an indication of the sourcing event template determined by the third trained artificial intelligence model (e.g. RFI suggested template is partially filled and presented to the user via graphical user interface, Col 4 lines 16-21, Fig 3, Col 3 lines 11-14).
Consider claim 14, Kumar discloses a system, comprising:
a computing device (computing device, Col 5 lines 40-50, Fig 5); and
a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations (computer processors execute instructions from e.g. ROM, Col 5 lines 52-62) comprising:
automatically identifying a free-form text message received by a user of a first organization (receiving free form text from a user, e.g. “Start a RFI on 30-th April and let it run till 30th October. Price will be in INR” and automatically classifying event type, dates, currency, etc., Col 5 lines 10-15, Fig. 2, Col 2-3 lines 59-2);
automatically determining, using a first trained artificial intelligence model, that the free-form text message corresponds to a request to create a sourcing event for a sourcing application (parsing the message and identifying type of event as RFP, RFI, etc., Col 2-3 lines 59-2, which is interpreted as a request to create a new sourcing object event as part of a supply chain procurement process, Col 2 lines 42-48; the parsing using a pre-trained language model, Col 3 lines 29-37);
determining to automatically create the sourcing event in the sourcing application (based on the parsed user input, selecting an appropriate template to create a new sourcing object event, Col 2 lines 42-48, Col 2-3 lines 59-10);
automatically extracting, from the free-form text message and using a second trained artificial intelligence model, sourcing event fields for the sourcing event (the parsed user-generated input is then used by a second machine learning model to determine a suggested template comprising a plurality of fields for initiating the event, Col 5 lines 32-48, Fig 4, the fields prepopulated based on the user input, Col 3 lines 11-13);
automatically determining, using a third trained artificial intelligence model, a sourcing event template for the sourcing event (machine learning model trained to select an optimal template for the sourcing event, Col 4-5 lines 52-9, the machine learning model that populates at least a portion of the fields being a different one than the one that selects the suggested template, Col 42-45; the model that suggests the template is therefore considered “a third trained artificial intelligence model”); and
automatically invoking an interface of the sourcing application to create the sourcing event in the sourcing application, including providing, to the interface, sourcing event fields extracted from the free-form text message and an indication of the sourcing event template determined by the third trained artificial intelligence model (e.g. RFI suggested template is partially filled and presented to the user via graphical user interface, Col 4 lines 16-21, Fig 3, Col 3 lines 11-14).
Consider claim 18, Kumar discloses a non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations (computer processors execute instructions from e.g. ROM, Col 5 lines 52-62) comprising:
automatically identifying a free-form text message received by a user of a first organization (receiving free form text from a user, e.g. “Start a RFI on 30-th April and let it run till 30th October. Price will be in INR” and automatically classifying event type, dates, currency, etc., Col 5 lines 10-15, Fig. 2, Col 2-3 lines 59-2);
automatically determining, using a first trained artificial intelligence model, that the free-form text message corresponds to a request to create a sourcing event for a sourcing application (parsing the message and identifying type of event as RFP, RFI, etc., Col 2-3 lines 59-2, which is interpreted as a request to create a new sourcing object event as part of a supply chain procurement process, Col 2 lines 42-48; the parsing using a pre-trained language model, Col 3 lines 29-37);
determining to automatically create the sourcing event in the sourcing application (based on the parsed user input, selecting an appropriate template to create a new sourcing object event, Col 2 lines 42-48, Col 2-3 lines 59-10);
automatically extracting, from the free-form text message and using a second trained artificial intelligence model, sourcing event fields for the sourcing event (the parsed user-generated input is then used by a second machine learning model to determine a suggested template comprising a plurality of fields for initiating the event, Col 5 lines 32-48, Fig 4, the fields prepopulated based on the user input, Col 3 lines 11-13);
automatically determining, using a third trained artificial intelligence model, a sourcing event template for the sourcing event (machine learning model trained to select an optimal template for the sourcing event, Col 4-5 lines 52-9, the machine learning model that populates at least a portion of the fields being a different one than the one that selects the suggested template, Col 42-45; the model that suggests the template is therefore considered “a third trained artificial intelligence model”); and
automatically invoking an interface of the sourcing application to create the sourcing event in the sourcing application, including providing, to the interface, sourcing event fields extracted from the free-form text message and an indication of the sourcing event template determined by the third trained artificial intelligence model (e.g. RFI suggested template is partially filled and presented to the user via graphical user interface, Col 4 lines 16-21, Fig 3, Col 3 lines 11-14).
Consider claim 2, Kumar discloses the free-form text message comprises an email message (e-mail, Col 5 lines 15-21).
Consider claim 3, Kumar discloses the free-form text message comprises a chat message (received via chatbot, Col 5 lines 15-21).
Consider claim 7, Kumar discloses sending a notification to the user regarding creation of the sourcing event in the sourcing application (the suggested template is presented to the user in a graphical user interface, Col 5 lines 46-48; this is considered notifying the user of the creation of the sourcing event, Col 4 lines 16-21, Fig 3, Col 3 lines 11-14).
Consider claim 9, Kumar discloses determining to automatically create the sourcing event in the sourcing application comprises: sending a request to the user that requests the user to respond whether the sourcing event should be automatically created (create sourcing project – request for information, Fig. 3, Col 5 lines 16-22); and receiving a response from the user indicating the sourcing event should be automatically created (user responds by clicking the “Create” button, Fig 3, Col 5 lines 16-22).
Consider claim 10, Kumar discloses the second trained artificial intelligence model comprises a sequence model (sequence model used to form event parameters, Col 5 lines 10-15).
Consider claim 11, Kumar discloses the second trained artificial intelligence model comprises a Softmax layer (the sequence model having a softmax at the end, Col 4 lines 9-14).
Consider claim 13, Kumar discloses the sourcing event fields for the sourcing event include event type, opening date, closing date, and currency (event type, start and closing date, and currency, Col 3 lines 44-48).
Consider claim 15, Kumar discloses the free-form text message comprises an email message (e-mail, Col 5 lines 15-21).
Consider claim 16, Kumar discloses the free-form text message comprises a chat message (received via chatbot, Col 5 lines 15-21).
Consider claim 19, Kumar discloses the free-form text message comprises a chat message (received via chatbot, Col 5 lines 15-21).
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 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 of this title, 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 4, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 10963636) in view of Chaudhary et al. (US 20220360579).
Consider claim 4, Kumar discloses the free-form text message is identified (receiving free form text from a user, e.g. “Start a RFI on 30-th April and let it run till 30th October. Price will be in INR” and automatically classifying event type, dates, currency, etc., Col 5 lines 10-15, Fig. 2, Col 2-3 lines 59-2).
Kumar does not specifically mention: after receiving consent from the user to automatically analyze free-form text messages received by the user.
Chaudhary discloses identifying free-form text messages after receiving consent from the user to automatically analyze free-form text messages received by the user (computing device requests and receives permission to analyze the emails of the user before identifying the emails, [0052-0056], Fig 5 steps 503-504).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kumar such that the free-form text message is identified after receiving consent from the user to automatically analyze free-form text messages received by the user in order to improve security, as suggested by Chaudhary ([0026]). Doing so would have led to predictable results of allowing users more readily available legitimate access, as suggested by Chaudhary ([0026]). The references cited are analogous art in the same field of natural language processing (Chaudhary, [0056]).
Consider claim 17, Kumar discloses the free-form text message is identified (receiving free form text from a user, e.g. “Start a RFI on 30-th April and let it run till 30th October. Price will be in INR” and automatically classifying event type, dates, currency, etc., Col 5 lines 10-15, Fig. 2, Col 2-3 lines 59-2).
Kumar does not specifically mention: after receiving consent from the user to automatically analyze free-form text messages received by the user.
Chaudhary discloses identifying free-form text messages after receiving consent from the user to automatically analyze free-form text messages received by the user (computing device requests and receives permission to analyze the emails of the user before identifying the emails, [0052-0056], Fig 5 steps 503-504).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kumar such that the free-form text message is identified after receiving consent from the user to automatically analyze free-form text messages received by the user for reasons similar to those for claim 4.
Consider claim 20, Kumar discloses the free-form text message is identified (receiving free form text from a user, e.g. “Start a RFI on 30-th April and let it run till 30th October. Price will be in INR” and automatically classifying event type, dates, currency, etc., Col 5 lines 10-15, Fig. 2, Col 2-3 lines 59-2).
Kumar does not specifically mention: after receiving consent from the user to automatically analyze free-form text messages received by the user.
Chaudhary discloses identifying free-form text messages after receiving consent from the user to automatically analyze free-form text messages received by the user (computing device requests and receives permission to analyze the emails of the user before identifying the emails, [0052-0056], Fig 5 steps 503-504).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kumar such that the free-form text message is identified after receiving consent from the user to automatically analyze free-form text messages received by the user for reasons similar to those for claim 4.
Claims 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 10963636) in view of Shukla (US 20250117665).
Consider claim 5, Kumar does not, but Shukla discloses the first trained artificial intelligence model, the second trained artificial intelligence model, and the third trained artificial intelligence model are trained for the first organization which results in determination of different weights for the first organization for the first trained artificial intelligence model, the second trained artificial intelligence model, and the third trained artificial intelligence model than weights determined for a second organization (custom LLMs are fine-tuned and trained on individual or organization specific data, [0096]; it is considered inherent that LLM instances trained on different data specific to different organizations result with different weights).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kumar such that the first trained artificial intelligence model, the second trained artificial intelligence model, and the third trained artificial intelligence model are trained for the first organization which results in determination of different weights for the first organization for the first trained artificial intelligence model, the second trained artificial intelligence model, and the third trained artificial intelligence model than weights determined for a second organization in order to better capture the nuances of training data, as suggested by Shukla ([0097]), predictably resulting in exceptionally valuable tools for organizations with specific data needs, as suggested by Shukla ([0097]). The references cited are analogous art in the same field of natural language processing.
Consider claim 12, Kumar does not, but Shukla discloses the second trained artificial intelligence model comprises a large language model (train an LLM, [0015]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kumar such that the second trained artificial intelligence model comprises a large language model for reasons similar to those for claim 5.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 10963636) in view of Midboe (US 20160350674).
Consider claim 6, Kumar discloses the first trained artificial intelligence model is a naïve Bayes model that is trained on a corpus of training data (Bayesian model, Col 5 lines 36-42).
Kumar does not specifically mention a naïve Bayes model.
Midboe discloses a naïve Bayes model (naïve Bayes classifier, [0050]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kumar by utilizing a naïve Bayes model in order to improve classification, predictably better assisting users, as suggested by Midboe [0003], [0008]). The references cited are analogous art in the same field of natural language processing.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Kumar et al. (US 10963636) in view of Lampert et al. (US 11010820).
Consider claim 8, Kumar discloses determining to automatically create the sourcing event in the sourcing application (based on the parsed user input, selecting an appropriate template to create a new sourcing object event, Col 2 lines 42-48, Col 2-3 lines 59-10)
Kumar does not specifically mention determining that a confidence level generated by the first trained artificial intelligence model is more than a threshold.
Lampert discloses determining that a confidence level generated by the first trained artificial intelligence model is more than a threshold (NLP system generates confidence level satisfying the threshold for intent identification, Col 4 lines 41-60, Col 10 lines 27-48).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Kumar by determining to automatically create the sourcing event in the sourcing application when a confidence level generated by the first trained artificial intelligence model is more than a threshold in order to remove obstacles for sourcing, as suggested by Lampert (Col 1 lines 37-42), predictably leaving less opportunities for customers to reconsider and abandon a procurement, as suggested by Lampert (Col 1 lines 37-42). The references cited are analogous art in the same field of natural language processing.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210201144 Jonnalagadda discloses artificial intelligence enhancements in automated conversations
US 20230186026 Arthur discloses data manufacturing frameworks for synthesizing synthetic training data to facilitate training a natural language to logical form model
US 12130923 Lee discloses augmenting training data using large language models
US 11804219 Gadde discloses entity level data augmentation in chatbots for robust named entity recognition
US 20230315722 Saxe discloses natural language interface for constructing complex database queries
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/Jesse S Pullias/
Primary Examiner, Art Unit 2655 07/30/26
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