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 correspondence 06/09/26 regarding application 18/752,949, in which claims 1, 7, 8, and 13 were amended, claim 16 was cancelled, and new claim 21 was added. Claims 1-15 and 17-21 are pending in the application and have been considered.
Response to Arguments
The examiner agrees with Applicant on page 9 that no new matter is added via the amendments to claims 1, 7, 8, and 13, and the addition of new claim 21.
The examiner agrees with Applicant’s page 9 characterization of the interview conducted April 28, 2026.
Amended independent claims 1, 8, and 13 overcome the nonstatutory obviousness-type double patenting rejections of claims 1-19 in the present application based on claims 1-10 and 12-18 of US Patent 12,056,448 in view of Tiwari, and so the double patenting rejections are withdrawn. Specifically, as amended the claims are sufficiently distinct from the claims of US Patent 12,056,448 so that the rejections are no longer applicable.
Applicant’s arguments on pages 10-11 regarding the previous 35 U.S.C. 103 rejections based on Tiwari, Plummer, Phan, and Relangi have been considered but are moot in view of new grounds for rejection based in part on the newly discovered references to Lad et al. (US 9917817), which corresponds to the newly claimed replacing an omitted sensitive data entry with a tokenized data entry generated by encrypting the omitted sensitive data entry with an encryption key in amended claims 1 and 8, and based in part on Perlmutter et al. (US 20150006881), which corresponds to the newly claimed temporary storage in RAM of amended claim 13. The new grounds for rejection based in part on Lad and Perlmutter are necessitated by Applicant’s amendments.
Claim Objections
The Examiner will assume, for examination purposes, that Applicant intended new claim 21 to depend on claim 5 rather than claim 1, since claim 5 would provide proper antecedent basis for the claimed “the graphical user interface” but claim 1 would not. Appropriate correction is required.
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 1, 3, 8, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (US 20220237567) in view of Plummer (US 20170132186), in further view of Lad et al. (US 9917817).
Consider claim 1, Tiwari discloses a system, comprising:
one or more processors (one or more processors, [0035]);
a Natural Language Processing (NLP) device (natural language processing module, [0043]);
a trained machine learning device (deep learning module based on machine learning, [0048]); and
a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors (application stored on memory and executed by a processor, [0064]), are configured to cause the system to:
receive, from a first user device, one or more documents and a first request (the chatbot receiving a request to apply for an opportunity, [0065], and images of documents of the user, [0066]);
extract one or more extractable data entries from the one or more documents based on the first request (extracting relevant information from the documents, [0066]);
generate, by the NLP device, one or more normalized data entries based on the one or more extractable data entries (performing OCR on applicant information from the imaging of the document, the generated characters considered to be normalized data entries for filling slots of the current decision tree requiring applicant information, [0077], for example, information that can be extracted from an image of a driver’s license and converted to a form accepted by slot filling node, [0074]);
generating and adding, by the trained machine learning device, one or more completed data entries in place of one or more placeholders in a first document template (using extracted image from the document to fill slot filling nodes of the current decision tree associated with the opportunity to apply, i.e. a document template specific to a particular opportunity, [0052], [0077], generated by retrieving a list of fields to apply for the requested opportunity from opportunities table, a document, [0065]).
Tiwari does not specifically mention receive, from a second user device, a second request and a first security identifier;
determine, by the trained machine learning device, a response to the second request;
alter, by the trained machine learning device, the response by omitting one or more sensitive data entries within the one or more normalized data entries based on the first security identifier;
provide the altered response to the second user device.
Plummer discloses receive, from a second user device, a second request and a first security identifier (end user of computer 105, a “second user device” in the computing network environment of Fig. 1, requests to access a PDF, which creates a request, i.e. “second request”, for the virtual PDF document at a particular authorization level, i.e. “first security identifier”, via identity manager module, [0133], [0134]);
determine, by the trained machine learning device, a response to the second request (page objects are located at 2020 via database 125, [0134]);
alter, by the trained machine learning device, the response by omitting one or more sensitive data entries based on the first security identifier (page objects are displayed based on authorization level of the user, such that there are multiple versions of the document at a number of authorization levels, [0134], where sensitive information is censored or redacted, [0143], by omitting redacted information from the pages, [0172]);
provide the altered response to the second user device (censored PDF page object is provided to the user for display on computer 105, [0132]).
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 Tiwari by receiving, from a second user device, a second request and a first security identifier; determining, by the trained machine learning device, a response to the second request; altering, by the trained machine learning device, the response by omitting one or more sensitive data entries, as in Plummer, within the one or more normalized data entries of Tiwari based on the first security identifier of Plummer; and providing the altered response to the second user device as in Plummer by generating and adding, by the trained machine learning device, one or more completed data entries in place of one or more placeholders in a first document template as in Tiwari in order to protect privileged content in documents, as suggested by Plummer ([0004]). Doing so would have led to predictable results of control of the content and dissemination of information contained in electronic documents, as suggested by Plummer ([0002]). The references cited are analogous art in the same field of document processing.
Tiwari and Plummer do not specifically mention replacing each omitted sensitive data entry with a tokenized data entry.
Lad discloses replacing each omitted sensitive data entry with a tokenized data entry (encryption/tokenization agent replaces the original plain text sensitive data with the encrypted/tokenized data, Col 4-5 lines 65-1).
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 Tiwari by replacing each omitted sensitive data entry with a tokenized data entry in order to address the challenges of protecting potentially sensitive data identified by Lad (Col 1 lines 11-21), predictably reducing the potential for information leakage, as suggested by Lad (Col 1 lines 11-21). The references cited are analogous art in the same field of document processing.
Consider claim 8, Tiwari discloses a system, comprising:
one or more processors (one or more processors, [0035]);
a Natural Language Processing (NLP) device (natural language processing module, [0043]);
a trained machine learning device (deep learning module based on machine learning, [0048]); and
a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors (application stored on memory and executed by a processor, [0064]), are configured to cause the system to:
receive, from a first user device, one or more documents and a first request (the chatbot receiving a request to apply for an opportunity, [0065], and images of documents of the user, [0066]);
extract one or more extractable data entries from the one or more documents based on the first request (extracting relevant information from the documents, [0066]);
generate, by the NLP device, one or more data entries based on the one or more extractable data entries (performing OCR on applicant information from the imaging of the document, the generated characters considered to be normalized data entries for filling slots of the current decision tree requiring applicant information, [0077], for example, information that can be extracted from an image of a driver’s license and converted to a form accepted by slot filling node, [0074]).
Tiwari does not specifically mention:
identify, by the trained machine learning device, one or more sensitive data entries within the one or more normalized data entries, each of the one or more sensitive data entries associated with a security tier of a plurality of security tiers;
receive, from a second user device, a second request and a first security identifier associated with a first security tier of a plurality of security tiers;
determine, by the trained machine learning device, a response to the second request;
alter, by the trained machine learning device, the response by omitting any sensitive data entry not associated with the first security tier;
provide the altered response to the second user device.
Plummer discloses identify, by the device, one or more sensitive data entries within the one or more data entries, each of the one or more sensitive data entries associated with a security tier of a plurality of security tiers (objects in page 806 are identified as having authorization level 807, 811, or 813, [0118], Fig. 8; source file objects may be Excel spreadsheets, i.e. the page objects are “sensitive data entries”, [0103], [0145]);
receive, from a second user device, a second request and a first security identifier associated with a first security tier of a plurality of security tiers (end user of computer 105, a “second user device” in the computing network environment of Fig. 1, requests to access a PDF, which creates a request, i.e. “second request”, for the virtual PDF document at a particular authorization level, i.e. “first security identifier” associated with a first security tier of a plurality of security tiers, via identity manager module, [0133], [0134]);
determine, by the device, a response to the second request (page objects are located at 2020 via database 125, [0134]);
alter, by the device, the response by omitting any sensitive data entry not associated with the first security tier (page objects are displayed based on authorization level of the user, such that there are multiple versions of the document at a number of authorization levels, [0134], where sensitive information is censored or redacted if they have an authorization level higher than that authorized for the user, [0143], by omitting redacted information from the pages, [0172]);
provide the altered response to the second user device (censored PDF page object is provided to the user for display on computer 105, [0132]).
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 Tiwari by identifying, as in Plummer, by the trained machine learning device of Tiwari, one or more sensitive data entries as in Plummer within the one or more normalized data entries of Tiwari, each of the one or more sensitive data entries associated with a security tier of a plurality of security tiers as in Plummer; receiving, from a second user device, a second request and a first security identifier associated with a first security tier of a plurality of security tiers; determining as in Plummer, by the trained machine learning device of Tiwari, a response to the second request as in Plummer; altering, as in Plummer, by the trained machine learning device of Tiwari, the response by omitting any sensitive data entry not associated with the first security tier as in Plummer; and providing the altered response to the second user device as in Plummer for reasons similar to those for claim 1.
Tiwari and Plummer do not specifically mention replacing each omitted sensitive data entry with a tokenized data entry generated by encrypting the omitted sensitive data entry with an encryption key.
Lad discloses replacing each omitted sensitive data entry with a tokenized data entry generated by encrypting the omitted sensitive data entry with an encryption key (encryption/tokenization agent replaces the original plain text sensitive data with the encrypted/tokenized data, Col 4-5 lines 65-1, the encrypted data generated using a symmetric key encryption algorithm, Col 7 lines 40-49).
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 Tiwari by replacing each omitted sensitive data entry with a tokenized data entry generated by encrypting the omitted sensitive data entry with an encryption key for reasons similar to those for claim 1.
Consider claim 3, Tiwari discloses the instructions are further configured to cause the system to: receive training feedback from the first user device (past interactions used for sequence learning, [0046]); and update the trained machine learning device using the training feedback (the chatbot system 222 learns from a sequence of interactions with client device 20 with deep learning module, [0046], [0048]).
Consider claim 10, Tiwari discloses the instructions are further configured to cause the system to: receive training feedback from the first user device (past interactions used for sequence learning, [0046]); and update the trained machine learning device using the training feedback (the chatbot system 222 learns from a sequence of interactions with client device 20 with deep learning module, [0046], [0048]).
Consider claim 11, Tiwari discloses the trained machine learning device comprises a recurrent neural network (RNN), a convolutional neural network (CNN), a transformer, or a combination thereof (RNN, [0059], Fig. 9, noting the claim language of “or” requires only one of the listed NN architectures).
Claims 13-15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (US 20220237567) in view of Phan et al. (US 11321519), in further view of Lad et al. (US 9917817), in further view of Perlmutter et al. (US 20150006881).
Consider claim 13, Tiwari discloses a system, comprising:
one or more processors (one or more processors, [0035]);
a Natural Language Processing (NLP) device (natural language processing module, [0043]);
a trained machine learning device (deep learning module based on machine learning, [0048]); and
a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors (application stored on memory and executed by a processor, [0064]), are configured to cause the system to:
receive, from a first user device, one or more documents (the chatbot receiving images of documents of the user, [0066]);
extract one or more extractable data entries from the one or more documents (extracting relevant information from the documents, [0066]);
generate, by the NLP device, one or more normalized data entries based on the one or more extractable data entries (performing OCR on applicant information from the imaging of the document, the generated characters considered to be normalized data entries for filling slots of the current decision tree requiring applicant information, [0077], for example, information that can be extracted from an image of a driver’s license and converted to a form accepted by slot filling node, [0074]).
Tiwari does not specifically mention receive, from a second user device, a second request; determine, by the trained machine learning device, (i) a first document template associated with the one or more normalized data entries and (ii) a first confidence interval based on the second request; responsive to the first confidence interval exceeding a predetermined threshold, generate and add, by the trained machine learning device, one or more completed data entries in place of one or more placeholders in the first document template; and responsive to the first confidence interval not exceeding the predetermined threshold: generate, by the NLP device, a response comprising a request for a user associated with the second user device to verify the first document template; and transmit the response to the second user device.
Phan discloses receive, from a second user device, a second request (user submits an electronic document to have the relevant information automatically entered into one or more data fields of a fillable electronic form, by user devices 102 over network 104, Col 3 lines 13-40);
determine, by the trained machine learning device, (i) a first document template associated with the one or more normalized data entries (fillable electronic form fillable with normalized information from the user’s CV, Col 11 lines 23-33, Fig. 4, the system utilizing machine learning, Abstract) and (ii) a first confidence interval based on the second request (e.g. interval defined by confidence ranges of fields automatically filled, Col 12 lines 31-63);
responsive to the first confidence interval exceeding a predetermined threshold, generate and add, by the trained machine learning device, one or more completed data entries in place of one or more placeholders in the first document template (values with a medium or high confidence are completed in the fields of the electronic form, Col 12 lines 25-63); and
responsive to the first confidence interval not exceeding the predetermined threshold (a confidence of “low” which below a threshold, Col 13 lines 17-32):
generate, by the NLP device, a response comprising a request for a user associated with the second user device to verify the first document template (for matches found with confidence lower than a threshold, e.g. “low”, the data verification service includes multiple matches and generates a prompt for the user to select one of them, Col 13 lines 17-32, data verification system 101 processing a user’s CV written in natural language, i.e. an NLP device, Col 5 lines 20-39); and
transmit the response to the second user device (the prompt is transmitted from data verification service 150 over network 104 to user devices 102, Fig 1, Col 13 lines 17-32).
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 Tiwari by receiving, from a second user device, a second request; determining, by the trained machine learning device, (i) a first document template associated with the one or more normalized data entries and (ii) a first confidence interval based on the second request; responsive to the first confidence interval exceeding a predetermined threshold, generating and adding, by the trained machine learning device, one or more completed data entries in place of one or more placeholders in the first document template; and responsive to the first confidence interval not exceeding the predetermined threshold: generating, by the NLP device, a response comprising a request for a user associated with the second user device to verify the first document template; and transmit the response to the second user device in order to reduce or eliminate the need to type into electronic forms, as suggested by Phan (Col 2 lines 31-33), predictably reducing difficulty for users of small display devices, as suggested by Phan (Col 2 lines 34-35). The cited references are analogous art in the field of filling out forms.
Tiwari and Phan do not specifically mention identifying one or more of the one or more extractable data entries as one or more sensitive data entries; replacing each omitted sensitive data entry with a tokenized data entry generated by encrypting the omitted sensitive data entry with an encryption key; omitting the one or more sensitive data entries from the one or more normalized data entries.
Lad discloses identifying one or more of the one or more extractable data entries as one or more sensitive data entries (Identifying one or more items of sensitive information from the outgoing data, e.g. a social security number, Col 7 lines 29-40);
replacing each omitted sensitive data entry with a tokenized data entry generated by encrypting the omitted sensitive data entry with an encryption key (encryption/tokenization agent replaces the original plain text sensitive data with the encrypted/tokenized data, Col 4-5 lines 65-1, the encrypted data generated using a symmetric key encryption algorithm, Col 7 lines 40-49);
omitting the one or more sensitive data entries from the one or more normalized data entries (the sensitive information items are replaced with the one or more items of encrypted sensitive information in the set of outgoing data, Col 41-49; this means the plain text sensitive items such as social security number are omitted, Col 4-5 lines 65-1).
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 Tiwari and Phan by identifying one or more of the one or more extractable data entries as one or more sensitive data entries; replacing each omitted sensitive data entry with a tokenized data entry generated by encrypting the omitted sensitive data entry with an encryption key as in Lad; responsive to the first confidence interval not exceeding the predetermined threshold of Phan, omitting the one or more sensitive data entries from the one or more normalized data entries as in Lad in order to address the challenges of protecting potentially sensitive data identified by Lad (Col 1 lines 11-21), predictably reducing the potential for information leakage, as suggested by Lad (Col 1 lines 11-21). The references cited are analogous art in the same field of document processing.
Tiwari, Phan, and Lad do not specifically mention storing the unencrypted sensitive data entry only temporarily in RAM such that only the tokenized data entry is permanently stored.
Perlmutter discloses storing an unencrypted sensitive data entry only temporarily in RAM such that only the encrypted data entry is permanently stored (the unencrypted key is stored temporarily in RAM, and encrypted key is permanently stored in non-volatile memory 202, Fig 6, [0074]-[0076]).
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 Tiwari, Phan, and Lad by storing the unencrypted sensitive data entry of Lad only temporarily in RAM as in Perlmutter such that only the tokenized data entry of Lad is permanently stored as in Perlmutter in order to automatically erase sensitive data when a user device is shut off, as suggested by Perlmutter ([0076]), predictably resulting in improved data confidentiality, as suggested by Perlmutter ([0002]-[0003]). The references cited are analogous art in the same field of document processing.
Consider claim 14, Tiwari discloses the instructions are further configured to cause the system to
receive, from the first user device, a natural language prompt (e.g. I would like to apply for the human resources direction position”, [0065]);
determine, by the NLP device, a machine-readable semantic representation of the natural language prompt (an intent associated with the request, Fig 8 step 326, [0050]);
Tiwari does not specifically mention responsive to the first confidence interval not exceeding the predetermined threshold, receiving a prompt;
update the first confidence interval based on the natural language prompt;
determine whether the updated first confidence interval exceeds the predetermined threshold; and responsive to the updated first confidence interval exceeding the predetermined threshold, proactively generate and add, by the trained machine learning device, the one or more completed data entries in place of the one or more placeholders in the first document template.
Phan discloses responsive to the first confidence interval not exceeding the predetermined threshold, receiving a prompt (a confidence of “low” which below a threshold, Col 13 lines 17-32, the user selects a presented candidate, i.e. prompts the system to fill in the field with that value, Col 13 lines 17-32);
update the first confidence interval based on a natural language prompt (updated confidences defining an interval on user interface with completed fields and confidence levels based on prompt, Fig 4, Col 16 lines 20-37;
determine whether the updated first confidence interval exceeds the predetermined threshold (values with a medium or high confidence in the fields of the electronic form, Col 12 lines 25-63); and responsive to the updated first confidence interval exceeding the predetermined threshold, proactively generate and add, by the trained machine learning device, the one or more completed data entries in place of the one or more placeholders in the first document template threshold (values with a medium or high confidence are completed in the fields of the electronic form, Col 12 lines 25-63).
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 Tiwari such that responsive to the first confidence interval not exceeding the predetermined threshold, receiving a prompt; updating the first confidence interval based on the natural language prompt; determining whether the updated first confidence interval exceeds the predetermined threshold; and responsive to the updated first confidence interval exceeding the predetermined threshold, proactively generate and add, by the trained machine learning device, the one or more completed data entries in place of the one or more placeholders in the first document template for reasons similar to those for claim 13.
Consider claim 15, Tiwari does not, but Phan discloses the instructions are further configured to cause the system to: generate a graphical user interface providing a visual representation of the first document template and the one or more completed data entries (user interface shows completed fields and confidence levels, Fig 4, Col 16 lines 20-37); and
transmit the graphical user interface to the first user device for display (outputting user interface showing completed fields and confidence levels to user devices 102 over network 104, Fig 4, Col 16 lines 20-37, Fig. 1).
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 Tiwari such that the instructions are further configured to cause the system to: generate a graphical user interface providing a visual representation of the first document template and the one or more completed data entries and transmit the graphical user interface to the first user device for display for reasons similar to those for claim 13.
Consider claim 18, Tiwari discloses the instructions are further configured to cause the system to: receive training feedback from the first user device (past interactions used for sequence learning, [0046]); and update the trained machine learning device using the training feedback (the chatbot system 222 learns from a sequence of interactions with client device 20 with deep learning module, [0046], [0048]).
Consider claim 19, Tiwari discloses the one or more completed data entries are generated based on the one or more normalized data entries (performing OCR on applicant information from the imaging of the document, the generated characters considered to be normalized data entries for filling slots of the current decision tree requiring applicant information, [0077], for example, information that can be extracted from an image of a driver’s license and converted to a form accepted by slot filling node, [0074]).
Claims 2, 5-7, 9, 12, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (US 20220237567) in view of Plummer (US 20170132186), in further view of Lad et al. (US 9917817), in further view of Phan et al. (US 11321519).
Consider claim 2, Tiwari does not, but Plummer discloses the first security identifier is associated with a first security tier, the first user device is associated with the first security tier (end user of computer 115, a “first user device” in the computing network environment of Fig. 1, has a particular authorization level, i.e. first security tier, [0133], [0134]), a second user device is associated with a second security tier (end user of computer 105, a “first user device” in the computing network environment of Fig. 1, has a particular authorization level, i.e. second security tier, [0133], [0134]), and the instructions are further configured to cause the system to:
identify a first sensitive data entry of the one or more completed data entries, the first sensitive data entry being associated with the first security tier (objects in page 806 are identified as having e.g. authorization level 807, [0118], Fig. 8; source file objects may be Excel spreadsheets, i.e. the page objects are “sensitive data entries”, [0103], [0145]);
identify a second sensitive data entry of the one or more completed data entries, the second sensitive data entry being associated with the second security tier (objects in page 806 are identified as having authorization level 811, [0118], Fig. 8; source file objects may be Excel spreadsheets, i.e. the page objects are “sensitive data entries”, [0103], [0145]).
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 Tiwari such that the first security identifier is associated with a first security tier, the first user device is associated with the first security tier, a second user device is associated with a second security tier, and the instructions are further configured to cause the system to: identify a first sensitive data entry of the one or more completed data entries, the first sensitive data entry being associated with the first security tier, and identify a second sensitive data entry of the one or more completed data entries, the second sensitive data entry being associated with the second security tier for reasons similar to those for claim 1.
Tiwari, Plummer, and Lad do not specifically mention: generate a first natural language response comprising the first sensitive data entry and a request to verify the first sensitive data entry; generate a second natural language response comprising the second sensitive data entry and a request to verify the second sensitive data entry; transmit the first natural language response to the first user device associated with the first security tier; and transmit the second natural language response to the second user device associated with the second security tier.
Phan discloses generate a first natural language response comprising the first data entry and a request to verify the first data entry (e.g. “This institution does not seem to exist” and prompt the user to select a match, Col 13 lines 17-33); generate a second natural language response comprising the second data entry and a request to verify the second data entry (e.g. for a second field, “We could not find a match in our database”, Col 13 lines 17-33); transmit the first natural language response to the first user device (the first prompt is transmitted from data verification service 150 over network 104 to a first of user devices 102, e.g. the laptop, Fig 1, Col 13 lines 17-32); and transmit the second natural language response to the second user device (the second prompt is transmitted from data verification service 150 over network 104 to a second of user devices 102, e.g. the desktop computer, Fig 1, Col 13 lines 17-32).
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 Tiwari, Plummer, and Lad by generating a first natural language response as in Phan comprising the first sensitive data entry of Plummer and a request to verify, as in Phan the first sensitive data entry of Plummer; generate a second natural language response as in Phan comprising the second sensitive data entry of Plummer and a request to verify, as in Phan the second sensitive data entry of Plummer; transmit the first natural language response to the first user device as in Phan associated with the first security tier of Plummer; and transmit the second natural language response to the second user device as in Phan associated with the second security tier of Plummer in order to reduce or eliminate the need to type into electronic forms, as suggested by Phan (Col 2 lines 31-33), predictably reducing difficulty for users of small display devices, as suggested by Phan (Col 2 lines 34-35).
Consider claim 5, Tiwari, Plummer, and Lad do not, but Phan discloses the instructions are further configured to cause the system to: generate a graphical user interface providing a visual representation of the first document template and the one or more completed data entries (user interface shows completed fields and confidence levels, Fig 4, Col 16 lines 20-37); and
transmit the graphical user interface to the first user device for display (outputting user interface showing completed fields and confidence levels to user devices 102 over network 104, Fig 4, Col 16 lines 20-37, Fig. 1).
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 Tiwari, Plummer, and Lad such that the instructions are further configured to cause the system to: generate a graphical user interface providing a visual representation of the first document template and the one or more completed data entries and transmit the graphical user interface to the first user device for display for reasons similar to those for claim 2.
Consider claim 6, Tiwari discloses the instructions are further configured to cause the system to: identify the first document template based on the first request and the one or more normalized data entries (based on determined apply to intent from a request to apply for an opportunity, decision tree includes nodes that indicated different application information fields or slots, retrieved as a list from opportunities table 232 for the particular opportunity, [0065]); this list considered a document and the nodes/slots considered a template; and performing OCR on applicant information from the imaging of the document, the generated characters considered to be normalized data entries for filling slots of the current decision tree requiring applicant information, [0077], for example, information that can be extracted from an image of a driver’s license and converted to a form accepted by slot filling node, [0074]).
Tiwari, Plummer, and Lad do not specifically mention: identify a confidence interval associated with the first document template, wherein generating and adding the one or more completed data entries in place of the one or more placeholders in the first document template is performed in response to determining the confidence interval exceeds a predetermined threshold.
Phan discloses identify a confidence interval associated with a first document template (e.g. interval defined by confidence ranges of fields automatically filled in one or more data fields of a fillable electronic form, Col 12 lines 31-63); wherein generating and adding the one or more completed data entries in place of the one or more placeholders in the first document template is performed in response to determining the confidence interval exceeds a predetermined threshold (values with a medium or high confidence are completed in the fields of the electronic form, Col 12 lines 25-63).
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 Tiwari, Plummer, and Lad by identifying a confidence interval associated with the first document template, wherein generating and adding the one or more completed data entries in place of the one or more placeholders in the first document template is performed in response to determining the confidence interval exceeds a predetermined threshold for reasons similar to those for claim 2.
Consider claim 7, Tiwari, Plummer, and Lad do not, but Phan discloses the instructions are further configured to cause the system to: responsive to determining the confidence interval does not exceed the predetermined threshold, generate, by the NLP device, an NLP response comprising a request for a one or more recipients to verify the first document template (for matches found with confidence lower than a threshold, e.g. “low”, the data verification service includes multiple matches and generates a prompt for the user to select one of them, Col 13 lines 17-32, data verification system 101 processing a user’s CV written in natural language, i.e. an NLP device, Col 5 lines 20-39).
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 Tiwari, Plummer, and Lad by responsive to determining the confidence interval does not exceed the predetermined threshold, generating, by the NLP device, an NLP response comprising a request for one or more recipients to verify the first document template for reasons similar to those for claim 2.
Consider claim 9, Tiwari, Plummer, and Lad do not, but Phan discloses the instructions are further configured to cause the system to: generate a graphical user interface providing a visual representation of the first document template and the one or more completed data entries included in the altered response and a respective confidence interval associated with each normalized data entry included in the altered response (user interface shows completed fields and confidence levels, Fig 4, Col 16 lines 20-37); and
transmit the graphical user interface to the first user device for display (outputting user interface showing completed fields and confidence levels to user devices 102 over network 104, Fig 4, Col 16 lines 20-37, Fig. 1).
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 Tiwari, Plummer, and Lad such that the instructions are further configured to cause the system to: generate a graphical user interface providing a visual representation of the first document template and the one or more completed data entries included in the altered response and a respective confidence interval associated with each normalized data entry included in the altered response and transmit the graphical user interface to the first user device for display for reasons similar to those for claim 2.
Consider claim 12, Tiwari, Plummer, and Lad disclose the system of claim 8 (see claim 8). Tiwari further discloses a second request comprises a natural language prompt (e.g. I would like to apply for the human resources direction position”, [0065]) and determining the response to the second request comprises determining, by the trained machine learning device, a second response to a machine-readable semantic representation of the natural language prompt (bot management system generates a response to the request based on the intent associated with the request, Fig 8 step 326, [0050], the extracted intent considered a machine readable semantic representation of the user request).
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 Tiwari and Plummer by utilizing a second request comprising a natural language prompt as in Tiwari, and such that determining the response to the second request comprises determining, by the trained machine learning device, a second response to a machine-readable semantic representation of the natural language prompt in order to reduce user effort, as suggested by Tiwari ([0007]).
Consider claim 21, Tiwari, Plummer, and Lad do not, but Phan discloses the graphical user interface includes the one or more completed data entries, wherein each completed data entry is highlighted in a color corresponding to an associated confidence interval for the respective completed data entry (green, yellow, or red based on confidence level, Col 11 lines 23-53).
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 Tiwari, Plummer, and Lad such that the graphical user interface includes the one or more completed data entries, wherein each completed data entry is highlighted in a color corresponding to an associated confidence interval for the respective completed data entry for reasons similar to those for claim 2.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (US 20220237567) in view of Plummer (US 20170132186), in further view of Lad et al. (US 9917817), in further view of Relangi (US 10635751).
Consider claim 4, Tiwari, Plummer, and Lad do not, but Relangi discloses training feedback comprises a number of corrected inputs received from the first device and updating the trained machine learning device further comprises comparing the corrected inputs to the one or more completed data entries (received corrected pseudo labeled named entities from the user via the UI and use the corrected entry as labeled data for training the recognition model, Col 2 lines 37-47).
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 Tiwari, Plummer, and Lad such that training feedback comprises a number of corrected inputs received from the first device and updating the trained machine learning device further comprises comparing the corrected inputs to the one or more completed data entries in order to address the intensive labeling and training processes associated with data scraping for conventional natural language chatbot training, as suggested by Relangi (Col 1 lines 34-46). Doing so would have led to predictable results of facilitating the chatbot to respond to a broad variety of queries and adapt to new queries not yet labeled in training data, as suggested by Relangi (Col 1 lines 47-52). The references cited are analogous art in the same field of natural language.
Claims 17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tiwari et al. (US 20220237567) in view of Phan et al. (US 11321519), in further view of Lad et al. (US 9917817), in further view of Perlmutter et al. (US 20150006881), in further in view of Plummer (US 20170132186).
Consider claim 17, Tiwari does not, but Phan discloses generate a first natural language response comprising the first data entry and a request to verify the first data entry (e.g. “This institution does not seem to exist” and prompt the user to select a match, Col 13 lines 17-33); generate a second natural language response comprising the second data entry and a request to verify the second data entry (e.g. for a second field, “We could not find a match in our database”, Col 13 lines 17-33); transmit the first natural language response to the first user device (the first prompt is transmitted from data verification service 150 over network 104 to a first of user devices 102, e.g. the laptop, Fig 1, Col 13 lines 17-32); and transmit the second natural language response to the second user device (the second prompt is transmitted from data verification service 150 over network 104 to a second of user devices 102, e.g. the desktop computer, Fig 1, Col 13 lines 17-32).
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 Tiwari by generating a first natural language response comprising the first data entry and a request to verify the first data entry, generating a second natural language response comprising the second data entry and a request to verify the second data entry, transmitting the first natural language response to the first user device, and transmitting the second natural language response to the second user device for reasons similar to those for claim 13.
Tiwari, Phan, Lad, and Perlmutter do not specifically mention generating a first natural language response comprising the first sensitive data entry and a request to verify the first sensitive data entry; generating a second natural language response comprising the second sensitive data entry and a request to verify the second sensitive data entry; transmitting the first natural language response to the first user device associated with the first security tier; and transmitting the second natural language response to the second user device associated with the second security tier.
Plummer discloses the first security identifier is associated with a first security tier, the first user device is associated with the first security tier (end user of computer 115, a “first user device” in the computing network environment of Fig. 1, has a particular authorization level, i.e. first security tier, [0133], [0134]), a second user device is associated with a second security tier (end user of computer 105, a “first user device” in the computing network environment of Fig. 1, has a particular authorization level, i.e. second security tier, [0133], [0134]), and the instructions are further configured to cause the system to:
identify a first sensitive data entry of the one or more completed data entries, the first sensitive data entry being associated with the first security tier (objects in page 806 are identified as having e.g. authorization level 807, [0118], Fig. 8; source file objects may be Excel spreadsheets, i.e. the page objects are “sensitive data entries”, [0103], [0145]);
identify a second sensitive data entry of the one or more completed data entries, the second sensitive data entry being associated with the second security tier (objects in page 806 are identified as having authorization level 811, [0118], Fig. 8; source file objects may be Excel spreadsheets, i.e. the page objects are “sensitive data entries”, [0103], [0145]).
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 Tiwari, Phan, Lad, and Perlmutter by generating a first natural language response comprising the first sensitive data entry and a request to verify the first sensitive data entry; generating a second natural language response comprising the second sensitive data entry and a request to verify the second sensitive data entry; transmitting the first natural language response to the first user device associated with the first security tier; and transmitting the second natural language response to the second user device associated with the second security tier in order to protect privileged content in documents, as suggested by Plummer ([0004]). Doing so would have led to predictable results of control of the content and dissemination of information contained in electronic documents, as suggested by Plummer ([0002]).
Consider claim 20, Tiwari does not, but Phan discloses receiving the second request, wherein the one or more completed data entries are generated (user submits an electronic document to have the relevant information automatically entered into one or more data fields of a fillable electronic form, by user devices 102 over network 104, Col 3 lines 13-40).
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 Tiwari by the second request, wherein the one or more completed data entries are generated for reasons similar to those for claim 13.
Tiwari, Phan, Lad, and Perlmutter do not specifically mention a first security identifier.
Plummer discloses a first security identifier (end user of computer 115, a “first user device” in the computing network environment of Fig. 1, has a particular authorization level, i.e. first security tier, [0133], [0134]).
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 Tiwari, Phan, Lad, and Perlmutter by including a first security identifier as in Plummer, and wherein the one or more completed data entries are generated as in Phan based on the first security identifier of Plummer for reasons similar to those for claim 17.
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.
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/Jesse S Pullias/
Primary Examiner, Art Unit 2655 07/16/26
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