DETAILED ACTION
This FINAL action is in response to Application No. 18/670,319 filed 5/21/2024. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The amendment presented on 7/29/2026 which provides amendments to claims 1, 3, 8, 10-15, and 17, and cancelation of claims 2, 9, and 16, is hereby acknowledged. Claims 1, 3-8, 10-15, and 17-20 are currently pending.
Claim Rejections – Withdrawn
The previous 35 U.S.C §112(b) rejection of claims 3, 10, and 17 is withdrawn as necessitated by amendment.
The previous 35 U.S.C §101 rejection of claims 1, 4-8, 11-15 and 18-21 over is withdrawn as necessitated by amendment.
The previous 35 U.S.C §102 rejection of claims 1, 4-8, 11-15 and 18-21 over Kundu et al. (US 2024/0071047 A1, published 2/29/2024) is withdrawn as necessitated by amendment.
Response to Arguments
Applicant’s arguments with respect to the prior art rejections have been considered, however, they are unpersuasive.
First, Applicant contends Kundu does not disclose or suggest “looking up a client configuration of the client system by the AI model.”
However, first, the “look up” step is not connected to any particular part of the process in the independent claims. Particularly, nothing in the “look up” limitation it is associated with, or referenced by, either of the “evaluating” or “determining” steps. Second, “client configuration” is never defined in the disclosure. At best, paragraph [0027] indicates that form fields may have different wording (“CountyTax” vs “(Name of County)Tax”) because of different client configurations, however, this gives no guidance on what “client configuration” means or includes. Therefore, the broadest reasonable interpretation of “looking up a client configuration of the client system by the AI model” is, as part of the process for identifying fields of a form, looking up / identifying a domain/type of form received from a client device. Each of the models in Kundu are trained machine learning models (Kundu, [0022]-[0026]) and determining the domain/form type is part of the process (“layer 0”) and different domains/form/types use different wording such as “‘Employee ID’, ‘Employee ID No.’, ‘Employee Identification Number’” (Kundu, [0031]-[0033]). Therefore, Kundu teaches the “look up” limitation.
Second, Applicant contends Kundu does not disclose or suggest “evaluating a client form structure comprising one or more generic fields for a client system of the plurality of client systems in a form specific language.”
However, first, the specification gives only an open-ended example of a “form specific language” at paragraphs [0018], [0024], [0034], and [0041]. The specification does not lock down the definition of “form specific language”. Particularly, it appears paragraph [0044] depicts character stings that are considered “form specific language”. Second, contrary to the assertion by Applicant and based on the structure of the limitation, the limitation is interpretable as stating that the “one or more generic fields” are “in a form specific language.” As shown in the citations elaborating below, the extracted fields/values formats can be character strings, other text data formats, or integers or other non-text formats that can be analyzed using language models as described herein (Kundu, [0019], [0030], e.g., “[{“Key”: “Employee Identification Number (EIN)”, “Value”: “A123”}]”). Therefore, Kundu is maintained for the limitations in question.
Applicant has not provided prior art arguments pertaining to the limitations incorporated into the independent claims which were rejected over the combination of Kundu and Dettman, which is maintained for the independent claims.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1, 4-8, 11-15 and 18-21, as best understood, is/are rejected under 35 U.S.C. 103 as being unpatentable over Kundu et al. (US 2024/0071047 A1, published 2/29/2024), and further in view of Dettman et al. (US 10,120,844 B2, hereinafter “Dettman”)
Regarding claim 1, Kundu teaches a method of automatic form conversion comprising:
receiving a plurality of client forms from a plurality of client systems. More specifically, Figure 1 depicts part of the process for mapping keys (fields) of forms to standard fields of standard forms (Kundu, abstract, [0002], [0027]-[0029]). Figure 2 depicts the process of training the machine learning model used in the mapping process (Kundu, [0045], [0051]).
evaluating a client form structure comprising one or more generic fields for a client system of the plurality of client systems in a form specific language by an artificial intelligence (AI) model, including analyzing one or more client forms from the client system by the AI model. More specifically, a form recognizer and machine learning models extract the field/value pairs and any variant words or phrases that may be semantically the same from forms in a specific format such as PDF. The extracted field/value pairs are described as character strings, other text data formats, or other types of data formats that can be analyzed using language models as described herein. Additionally, or alternatively, the field/value pairs are integers or other non-text formats. (Kundu, [0019], [0029], [0030], [0051], e.g., “[{“Key”: “Employee Identification Number (EIN)”, “Value”: “A123”}]”).
looking up a client configuration of the client system by the AI model. More specifically, the standardized field/value pairs and forms referenced and used by the models are customized for each client (Kundu, [0031]-[0034], [0051], [0055], [0064]-[0065], [0076], [0079]-[0080], e.g., looking up / determining a form type).
determining a relational map between the one or more generic fields in the client form structure for the client system and one or more standard fields in a standard form structure. More specifically, the field/value pairs are mapped to standard field/value pairs of a form type (standard form structure) by the machine learning models (Kundu, [0035], [0045], [0070]).
While Kundu generates mappings for each input form based on an identified (standard) form type, Kundu may not explicitly teach every aspect of using a previously created mapping for another form, or more specifically,
analyzing another client form from the client system by the AI model; and converting the other client form into the standard form structure based on the relational map.
Dettman discloses techniques for transforming input documents having disparate formats into a normalized (standard) format. According to one embodiment, a plurality of fields is identified in an input document that has a given format. Each field includes a descriptor and text content associated with the descriptor. For each field, semantic properties are evaluated for the descriptor and text content against a plurality of mapping rules to determine whether the field is consistent with one of a plurality of fields of a target format (Dettman, abstract; col 1, lines 11-14). The input documents, which are supplied from different sources (clients), are converted to the normalized (standard) documents based on previously established mapping rules (Dettman, col 3, lines 30-50; col 4, line 29-36; col 5, lines 29-34).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention given the teachings of Kundu and Dettman that a method for automatic form conversion where mappings between fields of client forms and fields of standard forms are established would include that a previously established mapping would be used to convert other forms. With Kundu and Dettman disclosing mapping fields of client forms to fields of standard forms, and with Dettman additionally disclosing the input documents, which are supplied from different sources (clients), are converted to the normalized (standard) documents based on previously established mapping rules, one of ordinary skill in the art of implementing a method for automatic form conversion where mappings between fields of client forms and fields of standard forms are established would include that a previously established mapping would be used to convert other forms in order to save on form processing where the mappings can be re-used. One would therefore be motivated to combine these teachings as in doing so would create this method for automatic form conversion where mappings between fields of client forms and fields of standard forms are established.
Regarding claim 4, Kundu and Dettman teach the method of claim 1, wherein said analyzing the one or more client forms from the client system by the AI model comprises identifying the one or more generic fields configured to represent one or more common items in the client form structure by the AI model, and wherein said determining the relational map comprises mapping between the one or more generic fields in the client form structure and the corresponding one or more standard fields configured to represent the one or more common items in the standard form structure. More specifically, for different form documents, different versions of a same form document, or the same form document but provided by different parties, different key formats are used for a common item. The disclosure includes constructing a Knowledge Graph containing popular standard formats of keys (e.g., “standard keys”) and developing machine learning models and associated processes to automatically map an extracted key (e.g., a “Custom Key” or “Input Key”) to a Standard Key (Kundu, [0020]-[0021], [0030]).
Regarding claim 5, Kundu and Dettman teach the method of claim 4, wherein the client form structure is common across the plurality of client systems including a first client system and a second client system of the plurality of client systems identified as a client system field in the client form structure, and wherein at least one or more generic fields configured to store corresponding one or more common items in the client form structure for the first client system are different from at least one or more generic fields configured to store the corresponding at least one or more items in the client form structure for the second client system. More specifically, for different form documents, different versions of a same form document, or the same form document but provided by different parties, different key formats are used for a common item. The disclosure includes constructing a Knowledge Graph containing popular standard formats of keys (e.g., “standard keys”) and developing machine learning models and associated processes to automatically map an extracted key (e.g., a “Custom Key” or “Input Key”) to a Standard Key (Kundu, [0020]-[0021], [0030]).
Regarding claim 6, Kundu and Dettman teach the method of claim 1, further comprising: determining by the AI model at least one of meaning and context associated with each generic field of the one or more generic fields based on the client configuration. More specifically, the process of using AI to map fields of an input form to fields of a standardized form includes determining a context - identifying an input domain/form type associated with standard form types (“layer 0” – custom client configuration - Kundu, [0031]-[0034], [0051], [0055], [0064]-[0065], [0076], [0079]-[0080]), and from there ranking pairings of input fields and standard fields on the likelihood of having the same meaning (“layer 2”) (Kundu, [0035]-[0037], [0040]-[0042], Figure 1). The AI models are trained with popular field phrases and domain specific phrases used for standard fields of standard forms (Kundu, [0045]-[0048]). The AI models are capable of determining the semantic meaning of words or phrases, and specifically whether the input fields are determined to be semantically similar or the same as standard fields (Kundu, [0050], [0073]).
Regarding claim 7, Kundu and Dettman teach the method of claim 6, wherein said determining the at least one of meaning and context comprises: analyzing one or more client text strings in each generic field based on the client configuration; and associating each generic field with the at least one of meaning and context represented by the one or more client text strings. More specifically, the process of using AI to map fields of an input form to fields of a standardized form includes determining a context - identifying an input form type associated with standard form types (“layer 0”), and from there ranking pairings of input fields and standard fields on the likelihood of having the same meaning (“layer 2”) (Kundu, [0035]-[0037], [0040]-[0042], Figure 1). The AI models are trained with popular field phrases and domain specific phrases used for standard fields of standard forms (Kundu, [0045]-[0048]). The AI models are capable of determining the semantic meaning of words or phrases, and specifically whether the input fields are determined to be semantically similar or the same as standard fields (Kundu, [0050], [0073]).
Regarding claims 8 and 11-14, these claims recite the non-transitory computer-readable storage medium with instructions for performing the steps of the method of claims 1 and 4-7, therefore, the same rationale of rejection is applicable.
Regarding claims 15 and 18-21, these claims recite the system for performing the steps of the method of claims 1 and 4-7, therefore, the same rationale of rejection is applicable.
Claim(s) 3, 10, and 17, as best understood, is/are rejected under 35 U.S.C. 103 as being unpatentable over Kundu and Dettman, and further in view of Oppenlander et al. (US 2006/0288269 A1, hereinafter “Oppenlander”).
Regarding claim 3, Kundu and Dettman teach the method of claim 1, however, may not explicitly teach every aspect of further comprising: printing the one or more generic fields in the other client form as the one or more standard fields in the standard form structure based on the relational map.
Oppenlander discloses a method for generating and delivering an electronic form to a user. The method involves mapping fields of standard form into a form file (Oppenlander, abstract). The completed form file is a format ready for printing (Oppenlander, [0048], [0067]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention given the teachings of Kundu and Dettman with Oppenlander that a method for automatic form conversion where mappings between fields of client forms and fields of standard forms are established would include printing the forms after the fields have been mapped to the fields of a standard form. With Kundu, Dettman, and Oppenlander disclosing mapping fields of client forms to fields of standard forms, and with Oppenlander additionally disclosing that mapping to standard forms is used for printing, one of ordinary skill in the art of implementing a method for automatic form conversion where mappings between fields of client forms and fields of standard forms are established would include printing the forms after the fields have been mapped to the fields of a standard form in order to have forms be consistent and correct versions when printed as required by banks and auto-dealerships. One would therefore be motivated to combine these teachings as in doing so would create this method for automatic form conversion where mappings between fields of client forms and fields of standard forms are established.
Regarding claim 10, this claim recites the non-transitory computer-readable storage medium with instructions for performing the steps of the method of claim 3, therefore, the same rationale of rejection is applicable.
Regarding claim 17, this claim recites the system for performing the steps of the method of claim 3, therefore, the same rationale of rejection is applicable.
Pertinent Prior Art
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.
Dua (US 2019/0087463 A1) – semantically mapping an input form to an electronic form standard.
Gormish (US 8,732,570 B2) – creating and using a mapping to fill forms.
D’Oria (US 11,406,455 B2) - converting legacy electronic forms and static electronic documents to web-fillable electronic forms.
Coquard (US 11,934,771 B2) - transforming an unstructured set of data representing a standardized form to a structured set of data.
Ye (US 8,850,304 B2) - mapping fields of various electronic forms to a common object model, uses the mapping data to automatically populate corresponding fields of the forms.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PATRICK F RIEGLER whose telephone number is (571)270-3625. The examiner can normally be reached M-F 9:30am-6:00pm, ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kieu Vu can be reached at (571) 272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PATRICK F RIEGLER/ Primary Examiner, Art Unit 2171
