DETAILED ACTION
This action is responsive to the following communication: the RCE filed on 09/16/2025. This action is made non-final.
Claims 1-10 are pending in the case. Claim 1 is independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 08/15/25 has been entered.
Response to Arguments
Applicant's arguments filed 08/15/2025 have been fully considered but they are moot in view of new ground of rejection.
With respect to claim rejection 35 U.S.C. § 101, the previous rejection is withdrawn in view of the amendment.
Claim Objections
Claims 1-10 are objected to because of the following informalities: the term FNOL recited in line 13-14 of claim 1 needs to be spelled out for clarity. Claims 5-8 recites the similar phrase that also needs to be spelled out.
Claim 7 further recites the term “STP” which should also be spelled out.
The dependent claims 2-4, 9-10 are objected as incorporating the deficiency of claim 1 upon which they depend.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 1-10 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claim 1, claim 1 recites “the determined value” in line 11 and line 17, “the fields” in line 12, “the completeness value” in line 13, and “the quality of the collected information” in line 15. There is a lack of antecedent basis for these limitations in the claim.
Regarding claim 4, claim 4 recites “the value detection module”. There is a lack of antecedent basis for this limitation in the claim because it is not clear what “value detection module” claim 4 refers to: is it the one recited in claim 1 or the one recited in claim 3.
Regarding claim 5, claim 5 recites “the completeness value”. There is a lack of antecedent basis for this limitation in the claim.
Regarding claim 6, claim 6 recites “the FNOL completeness index calculation module”. There is a lack of antecedent basis for this limitation in the claim because it is not clear what “FNOL completeness index calculation module” claim 6 refers to: is it the one recited in claim 1 or the one recited in claim 5.
Regarding claims 9-10, claims 9-10 recite “the index”. There is a lack of antecedent basis for this limitation in the claims.
The dependent claims 2-10 are rejected as incorporating the deficiency of claim 1 upon which they depend.
Claim 2 is rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 2 recites the limitations “wherein the completeness index is calculated using a completeness index calculation module”. The completeness index calculation module fails to further limits the “FNOL Completeness index calculation module” recited in claim 1. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Fernando et al. (NPL: “Automated vehicle insurance claims processing using computer vision, natural language processing”, published during 22nd International Conference on Advances in ICT on 11/30/2022; hereinafter Fernando) in view Feiterira et al. (US 11676215 B1; hereinafter Feiterira).
As to claim 1, Fernando teaches:
A method of receiving information from a first notice of a loss ([page 126] & Fig. 1, <section B>: Initial vehicle claims form filling using speech recognition) comprising
receiving a communication to report a loss ([page 126] & Fig. 1, <section B>: Initial vehicle claims form filling using speech recognition, voice input provided by the claimant);
storing the communication in a memory ([page 126] & Fig. 1 & <section B>, audio clip is stored in the memory on “client side” before being passed to the system of Fig. 1, via API gateway);
converting the communication to text using a voice transcription system ([page 126] & Fig. 1 & <section B> to <section C>, audio clip containing details of the accident is converted into text format using speech-to-text conversion in the system of Fig. 1);
determining whether the text relates to known fields using a field detection model ([pages 126-127] & Fig. 1 & <section B> to <section D>, converted text is classified using NLP and form fields are detected/determined, before the converted text is segmented and filled into to matching fields, thus, arranged in a form format, wherein the form includes known fields and the conversion system determines if text relates to known fields) wherein the field detection module utilizes artificial intelligence trained on successful past text inputs and specific known field determinations to determine if the text should be placed in a specific known field (([pages 126-127] & Fig. 1 & <section B> to <section D>; Details in the converted text paragraph are classified into nine fields; for the text classification process, SpaCy and NER models are used, relevant fields in the training data set were tagged using SpaCy NER annotator ..rule-based matching and training processes were carried out);
in response to the determining the text relates to a specific known field, using a value detection module for placing the determined value as text in detected field ([pages 126-127] & Fig. 1 & <section B> to <section D>, when converted text is arranged in a form format, text related to known fields are placed in detected fields upon matching text to known fields).
Fernando does not appear to teach:
once the fields are full, calculating completeness index, wherein the completeness value is determined using a FNOL Completeness Index Calculation Module and wherein the FNOL Completeness Index Calculation Module determines a numerical index to evaluate the quality of the collected information; and
based on the numerical index, determining if interactive questions are needed to fill in the fields and obtain an acceptable index score.
Feiterira is relied upon for teaching the deficient limitations. Specifically, Feiterira disclose a method for automatically processing a claim provided by a user (see Col. 1, lines 49-67). Feiterira teaches:
once the fields are full, calculating completeness index, wherein the completeness value is determined using a FNOL Completeness Index Calculation Module and wherein the FNOL Completeness Index Calculation Module determines a numerical index to evaluate the quality of the collected information (see Col. 15, line 55 through Col. 16, line 61; After collecting the set of customer identity validation data, at step/operation 404, a module in the claims processing system 120, such as FNOL engine 225, may determine whether the set of customer identity validation data meets pre-defined identity validation criteria. In some embodiments, pre-programmed business rules may assess each field, individually and/or collectively, in the customer identity validation data as to the level of adequacy of information provided to make a coverage determination for the loss, potential scope of damages, and potential indicators of claim complexity. Each field in the customer identity validation data may be assessed and assigned relative weighting based on historical proprietary and third party claim analytics data that correlate a set of potential answers for the field with eventual claim outcomes. Structured responses for each field may be assigned a numeric value depending on their potential impact on the claim; The FNOL engine 225 may aggregate the values from the individual field assessment to determine if the information provided could advance the claim to the next phase of processing, or if additional information would need to be gathered; If the aggregated score fell within a certain range determined to be adequate, the set of customer identity validation data meets the pre-defined identity validation criteria and the claim may progress. If the aggregated score fell within a predetermined range that was not adequate for progression, the set of customer identity validation data does not meet the pre-defined identity validation criteria and additional information may be requested); and
based on the numerical index, determining if interactive questions are needed to fill in the fields and obtain an acceptable index score (see Col. 15, line 55 through Col. 16, line 61; If the aggregated score fell within a predetermined range that was not adequate for progression, the set of customer identity validation data does not meet the pre-defined identity validation criteria and additional information may be requested).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, having seen the teachings of Fernando and Feiterira together, to have modified the feature of loss report as disclosed in Fernando to include the feature of checking to see if the form is completed as disclosed in Feiterira to achieve the claim limitation, with reasonable expectation of success. One would be motivated to make such a combination to ensure the readiness of FNOL data before proceeding to the next step in claim processing (Feiterira: See Col. 15, line 55 through Col. 16, line 61).
Regarding claim 2, the rejection of claim 1 is incorporated. Fernando/Feiterira further teach wherein the completeness index is calculated using a completeness index calculation module (Feiterira: See Col. 15, line 55 through Col. 16, line 61; Structured responses for each field may be assigned a numeric value depending on their potential impact on the claim; The FNOL engine 225 may aggregate the values from the individual field assessment to determine if the information provided could advance the claim to the next phase of processing, or if additional information would need to be gathered; If the aggregated score fell within a certain range determined to be adequate, the set of customer identity validation data meets the pre-defined identity validation criteria and the claim may progress. If the aggregated score fell within a predetermined range that was not adequate for progression, the set of customer identity validation data does not meet the pre-defined identity validation criteria and additional information may be requested). Combining Fernando and Feiterira would meet the claimed limitations for the same reasons as set forth in claim 1.
Regarding claim 3, the rejection of claim 1 is incorporated. Fernando/Feiterira further teach wherein placing the text in detected field further comprises using a Value Detection Module to determine a value to place in the field (Fernando: See page [0126] & <section D>, values for sample fields like driver’s name, driver’s NIC number, vehicle Model are determined and filled by one of the system modules in Fig. 1).
Regarding claim 4, the rejection of claim 3 is incorporated. Fernando/Feiterira further teach wherein the value detection module comprises a combination of machine learning techniques and rule-based algorithms (Fernando: See [0126] & <section C> & <section D> on deep learning and rule-based algorithms for speech-to-text and text analysis).
Regarding claim 5, the rejection of claim 1 is incorporated. Fernando/Feiterira further teach wherein the completeness value is determined using a FNOL Completeness Index Calculation Module (Feiterira: See Col. 15, line 55 through Col. 16, line 61; a module in the claims processing system 120, such as FNOL engine 225, may determine whether the set of customer identity validation data meets pre-defined identity validation criteria). Combining Fernando and Feiterira would meet the claimed limitations for the same reasons as set forth in claim 1.
Regarding claim 6, the rejection of claim 5 is incorporated. Fernando/Feiterira further teach wherein the FNOL Completeness Index Calculation Module Determines a numerical index to evaluate the quality of the collected information (Feiterira: See Col. 15, line 55 through Col. 16, line 61; In some embodiments, pre-programmed business rules may assess each field, individually and/or collectively, in the customer identity validation data as to the level of adequacy of information provided to make a coverage determination for the loss, potential scope of damages, and potential indicators of claim complexity. Each field in the customer identity validation data may be assessed and assigned relative weighting based on historical proprietary and third party claim analytics data that correlate a set of potential answers for the field with eventual claim outcomes. Structured responses for each field may be assigned a numeric value depending on their potential impact on the claim; The FNOL engine 225 may aggregate the values from the individual field assessment to determine if the information provided could advance the claim to the next phase of processing, or if additional information would need to be gathered; If the aggregated score fell within a certain range determined to be adequate, the set of customer identity validation data meets the pre-defined identity validation criteria and the claim may progress. If the aggregated score fell within a predetermined range that was not adequate for progression, the set of customer identity validation data does not meet the pre-defined identity validation criteria and additional information may be requested). Combining Fernando and Feiterira would meet the claimed limitations for the same reasons as set forth in claim 1.
Regarding claim 7, the rejection of claim 1 is incorporated. Fernando/Feiterira further teach wherein the FNOL Completeness Index Calculation module analyzes at least one of: a total number of FNOL fields filled, an accuracy of the detected values, importance of each field in the context of the STP prediction, and a presence of any required or conditional fields (Feiterira: See Col. 15, line 55 through Col. 16, line 61; Structured responses for each field may be assigned a numeric value depending on their potential impact on the claim; The FNOL engine 225 may aggregate the values from the individual field assessment to determine if the information provided could advance the claim to the next phase of processing, or if additional information would need to be gathered; If the aggregated score fell within a certain range determined to be adequate, the set of customer identity validation data meets the pre-defined identity validation criteria and the claim may progress. If the aggregated score fell within a predetermined range that was not adequate for progression, the set of customer identity validation data does not meet the pre-defined identity validation criteria and additional information may be requested). Combining Fernando and Feiterira would meet the claimed limitations for the same reasons as set forth in claim 1.
Regarding claim 8, the rejection of claim 7 is incorporated. Fernando/Feiterira further teach wherein the FNOL Completeness Index Calculation module employs a weighted scoring system that assigns greater importance to critical fields (Feiterira: see Col. 15, line 55 through Col. 16, line 61; After collecting the set of customer identity validation data, at step/operation 404, a module in the claims processing system 120, such as FNOL engine 225, may determine whether the set of customer identity validation data meets pre-defined identity validation criteria. In some embodiments, pre-programmed business rules may assess each field, individually and/or collectively, in the customer identity validation data as to the level of adequacy of information provided to make a coverage determination for the loss, potential scope of damages, and potential indicators of claim complexity. Each field in the customer identity validation data may be assessed and assigned relative weighting based on historical proprietary and third party claim analytics data that correlate a set of potential answers for the field with eventual claim outcomes. Structured responses for each field may be assigned a numeric value depending on their potential impact on the claim; The FNOL engine 225 may aggregate the values from the individual field assessment to determine if the information provided could advance the claim to the next phase of processing, or if additional information would need to be gathered; If the aggregated score fell within a certain range determined to be adequate, the set of customer identity validation data meets the pre-defined identity validation criteria and the claim may progress. If the aggregated score fell within a predetermined range that was not adequate for progression, the set of customer identity validation data does not meet the pre-defined identity validation criteria and additional information may be requested). Combining Fernando and Feiterira would meet the claimed limitations for the same reasons as set forth in claim 1.
Regarding claim 9, the rejection of claim 8 is incorporated. Fernando/Feiterira further teach wherein, based on the index, determining if interactive questions are needed to fill in the fields and obtain an acceptable index score (Feiterira: see Col. 15, line 55 through Col. 16, line 61; If the aggregated score fell within a predetermined range that was not adequate for progression, the set of customer identity validation data does not meet the pre-defined identity validation criteria and additional information may be requested). Combining Fernando and Feiterira would meet the claimed limitations for the same reasons as set forth in claim 1.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Fernando/Feiterira as applied to claim 1 above, and further in view of Campbell (US 2008/0065974).
Regarding claim 10, the rejection of claim 1 is incorporated. Fernando/Feiterira further teach wherein if the index is below a threshold: asking questions based on the category of the loss to fill in fnol fields; receiving responses to the questions to fill in the fields (Feiterira: see Col. 15, line 55 through Col. 16, line 61; If the aggregated score fell within a predetermined range that was not adequate for progression, the set of customer identity validation data does not meet the pre-defined identity validation criteria and additional information may be requested).
Fernando/Feiterira do not appear to teach in response to the text not being understood, using machine language to interpret the text and repeat the text that has been interpreted back; and based on the text that has been interpreted i and placed in the field, re- calculating the index.
However, the prior art of Campbell can be relied upon for a teaching of the limitation. Campbell is directed to providing an intuitive and integrated management tool for disparate communications channels (see abstract). Campbell teaches user input via a speech recognition program (see ¶ 0280). Campbell teaches providing help signal for input clarification (see ¶ 0028). Specifically, Campbell teaches in response to the text not being understood, using machine language to interpret the text and repeat it back (see ¶ 0032, a help signal 108 prompts a user 140 for further input or information including an indication that the input signal 104 was not understood (or was understood to be ambiguous), an interpretation of the received input signal 104 by speech recognition program in [0280], a request to verify the input signal 104, i.e., “repeat it back”, a help message with suggested alternative input signals, or any and all other helpful information, i.e., the combination thereof).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, having seen the teachings of Fernando/Feiterira and Campbell together, to have combined the teachings of iterative question-and-answer based on form completeness assessment approach for users to complete a FNOL filing and data readiness score or form completeness index calculation taught in Feiterira, and input clarification help signal taught by Campbell to achieve the claim limitation, with reasonable expectation of success. One would be motivated to make such a combination to ensure the readiness of FNOL data that matches user intent before proceeding to the next step in claim processing (Campbell: see ¶ 0032, user verified input).
Conclusion
The prior art made of record on form PTO-892 and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006,1009, 158 USPQ 275,277 (CCPA 1968)).
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/TUYETLIEN T TRAN/ Primary Examiner, Art Unit 2179