DETAILED ACTION
This communication is in response to the Amendments and Arguments filed on 01/20/2026.
Claims 6, 8, 16, and 18 have been canceled by the Applicant.
Claim(s) 1-5, 7, 9-15, 17, and 19-20 are pending and have been examined. Hence, this action has been made FINAL.
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 .
Response to Arguments and Amendments
Amendments to the claims by the Applicant have been considered and addressed below.
With respect to the 35 USC § 101, and 103 rejections, the Applicant provides several arguments in which the Examiner will respond accordingly, below.
35 USC § 103 rejection(s)
Arguments in pages 11-12 of the Remarks filed on 01/20/2026
Examiner’s Response to Arguments:
Applicant’s arguments with respect to claim(s) 1 and 11 under 35 U.S.C. § 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Lyubarskiy et al. (US 20160171627 A1) and further in view of Shen et al. (US 20190354578 A1) and Anand et al. (US 9652790 B2).
For more details, please refer to updated 35 U.S.C. § 103 rejections for claims 1 and 11, below.
35 USC § 101 rejection(s)
Arguments in pages 9-11 of the Remarks filed on 01/20/2026
Examiner response to Arguments:
Applicant’s arguments, with respect to the rejection(s) of independent claim(s) 1 and 11 under 35 USC 101 have been fully considered but are not persuasive.
The Applicant argues that:
The Claims Integrate Any Alleged Abstract Idea Into A Practical
Application At Least In View Of The Desjardins Memo.
The Desjardins memo clarifies that "the eligibility determinations should turn on whether the claims are directed to an improvement to computer functionality versus being directed to an abstract idea." Desjardins memo, 1 (quoting Enfish, LLC v. Microsoft Corp., Page 7
822 F.3d 1327, 1336, 1339 (Fed. Cir. 2016)) (internal quotation marks omitted). The Desjardins memo further expands the definition of computer technology to include software and stresses that software "can make non-abstract improvements to computer technology, just as hardware improvements can." Id.
The claims recite a non-abstract software improvement. The specific problem existing computer applications (i.e., computer software) faced was the users using different types of formatting of documents. See Spec., para. 3…
The Claims Also Recite A Significant Improvement Over Any Alleged
Abstract Idea In View Of DDR
In DDR, the Court distinguished between claims that "merely recite the performance of some business practice known from the pre-Internet world along with the requirement to perform it on the Internet" (patent ineligible) and the claims that are "necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks" (patent eligible). DDR at pg. 20…
However, the Examiner respectfully disagrees because:
A human is capable of performing actions associated with preknown / predefined set of steps or rules (i.e., models) as will be discussed below.
The Examiner refers the Applicant to MPEP 2106.05(a):
“It is important to note that in order for a method claim to improve computer functionality, the broadest reasonable interpretation of the claim must be limited to computer implementation. That is, a claim whose entire scope can be performed mentally, cannot be said to improve computer technology. Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 120 USPQ2d 1473 (Fed. Cir. 2016) (a method of translating a logic circuit into a hardware component description of a logic circuit was found to be ineligible because the method did not employ a computer and a skilled artisan could perform all the steps mentally). Similarly, a claimed process covering embodiments that can be performed on a computer, as well as embodiments that can be practiced verbally or with a telephone, cannot improve computer technology. See RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1328, 122 USPQ2d 1377, 1381 (Fed. Cir. 2017) (process for encoding/decoding facial data using image codes assigned to particular facial features held ineligible because the process did not require a computer).” (Emphasis added)
In the DDR Holdings’ involved claims disclosed limitations that recite:
(‘572 patent)
DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1249 (Fed. Cir. 2014)
(“13. An e-commerce outsourcing system comprising:
a) a data store including a look and feel description associated with a host web page having a link correlated with a commerce object; and
b) a computer processor coupled to the data store and in communication through the Internet with the host web page and programmed, upon receiving an indication that the link has been activated by a visitor computer in Internet communication with the host web page, to serve a composite web page to the visitor computer wit[h] a look and feel based on the look and feel description in the data store and with content based on the commerce object associated wit [h] the link.”)
As seen from the claim limitations in the (‘572) claim above, the system requires that the system provide a host website with a “link” that “correlate[s]” the host website with a “commerce object.” Also, the system presents the construction of a composite webpage comprising a “look and feel” based on a description in the data store and product information.
(DDR Holdings, LLC v. Hotels.com, https://cafc.uscourts.gov/opinions-orders/13-1505.opinion.12-3-2014.1.pdf)
(‘399 patent)
DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1249-50 (Fed. Cir. 2014)
(“19. A system useful in an outsource provider serving web pages offering commercial opportunities, the system comprising:
(a) a computer store containing data, for each of a plurality of first web pages, defining a plurality of visually perceptible elements, which visually perceptible elements correspond to the plurality of first web pages;
(i) wherein each of the first web pages belongs to one of a plurality of web page owners;
(ii) wherein each of the first web pages displays at least one active link associated with a commerce object associated with a buying opportunity of a selected one of a plurality of merchants; and
(iii) wherein the selected merchant, the out-source provider, and the owner of the first web page displaying the associated link are each third parties with respect to one other;
(b) a computer server at the outsource provider, which computer server is coupled to the computer store and programmed to:
(i) receive from the web browser of a computer user a signal indicating activation of one of the links displayed by one of the first web pages;
(ii) automatically identify as the source page the one of the first web pages on which the link has been activated;
(iii) in response to identification of the source page, automatically retrieve the stored data corresponding to the source page; and
(iv) using the data retrieved, automatically generate and transmit to the web browser a second web page that displays: (A) information associated with the commerce object associated with the link that has been activated, and (B) the plurality of visually perceptible elements visually corresponding to the source page.”)
Similarly, as seen from the claim limitations in the (‘399) claim above, the system requires a “data store” hold “visually perceptible” or “look and feel” elements that “visually” correspond to a host web page.
Hence, DDR’s claims are considered to “fall within section 101 because the “solution” they offer “is necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks.” Ante at 20.”
(DDR Holdings, LLC v. Hotels.com, https://cafc.uscourts.gov/opinions-orders/13-1505.opinion.12-3-2014.1.pdf)
In conclusion, as shown in the previous recitations of the cited case law (i.e., DDR Holdings’), these are all deemed rooted in computer technology. However, the Instant Application does not comprise similar recitations (i.e., implementation of a solution to a problem in computer technology: computer networks / software arts).
For example, the Instant Application recites:
1. (Original) A computer-implemented method, comprising:
extracting a plurality of strings from an electronic document comprising an invoice;
executing fuzzy matching between the extracted plurality of strings and a list of fields to map a first subset of the plurality of strings to a corresponding first subset of fields;
invoking a trained natural language processing model to map a second subset of the plurality of strings to a corresponding second subset of fields, the natural language processing model being trained by providing training questions, corresponding training context, and corresponding training answers, the invoking comprising providing a question and a corresponding context and receiving a corresponding answer;
combining the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of plurality of strings to the corresponding second subset of fields, the combining of the mapping comprising:
removing duplicates from the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of the plurality of strings to the corresponding second subset of fields, the removing of the duplicates comprising removing a mapping of at least one string of the first subset of the plurality of strings to an "other" field in the mapping between the first subset of plurality of strings to the corresponding first subset of fields, the at least one string being similar to a second string in the mapping between the second subset of the plurality of strings and the corresponding second subset of fields; and
automatically generating an electronic template of the invoice using the combined mapping.
Therefore, the Instant Application is not rooted in computer technology, but rather on implementing an abstract idea in natural language processing, more specifically in the field of text/template generation. More details on the rationale used to examine the claims rejected under 35 U.S.C. § 101 of the Instant Application are provided below for clarification.
Please see detailed analysis below (Prong Two) for more details on how the Examiner understands the independent claims do not recite additional elements that integrate the judicial exception into a practical application. Hence, not qualifying as patent eligible subject matter under 35 U.S.C. § 101.
Please refer to MPEP 2106.04(1): Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception: Prong One.
“Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon? In Prong One examiners evaluate whether the claim recites a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. While the terms "set forth" and "described" are thus both equated with "recite", their different language is intended to indicate that there are two ways in which an exception can be recited in a claim. For instance, the claims in Diehr, 450 U.S. at 178 n. 2, 179 n.5, 191-92, 209 USPQ at 4-5 (1981), clearly stated a mathematical equation in the repetitively calculating step, and the claims in Mayo, 566 U.S. 66, 75-77, 101 USPQ2d 1961, 1967-68 (2012), clearly stated laws of nature in the wherein clause, such that the claims "set forth" an identifiable judicial exception. Alternatively, the claims in Alice Corp., 573 U.S. at 218, 110 USPQ2d at 1982, described the concept of intermediated settlement without ever explicitly using the words "intermediated" or "settlement."”
“An example of a claim that recites a judicial exception is "A machine comprising elements that operate in accordance with F=ma." This claim sets forth the principle that force equals mass times acceleration (F=ma) and therefore recites a law of nature exception. Because F=ma represents a mathematical formula, the claim could alternatively be considered as reciting an abstract idea. Because this claim recites a judicial exception, it requires further analysis in Prong Two in order to answer the Step 2A inquiry. An example of a claim that merely involves, or is based on, an exception is a claim to "A teeter-totter comprising an elongated member pivotably attached to a base member, having seats and handles attached at opposing sides of the elongated member." This claim is based on the concept of a lever pivoting on a fulcrum, which involves the natural principles of mechanical advantage and the law of the lever. However, this claim does not recite these natural principles and therefore is not directed to a judicial exception (Step 2A: NO). Thus, the claim is eligible at Pathway B without further analysis.”
From this analysis, in Step 2A, Prong One, the Examiner has evaluated the independent claims accordingly and determined that the amended independent claims as drafted indeed describe a judicial exception (i.e., an abstract idea), which represent a mental process (which can be performed by a human with pen and paper).
More specifically, similar to what was discussed in the Non-Final Rejection mailed on 10/23/2025:
The limitations as drafted cover a human (mental process).
More specifically, the independent claim(s) recite(s):
1. (Original) A computer-implemented method, comprising:
extracting a plurality of strings from an electronic document comprising an invoice;
executing fuzzy matching between the extracted plurality of strings and a list of fields to map a first subset of the plurality of strings to a corresponding first subset of fields;
invoking a trained natural language processing model to map a second subset of the plurality of strings to a corresponding second subset of fields, the natural language processing model being trained by providing training questions, corresponding training context, and corresponding training answers, the invoking comprising providing a question and a corresponding context and receiving a corresponding answer;
combining the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of plurality of strings to the corresponding second subset of fields, the combining of the mapping comprising:
removing duplicates from the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of the plurality of strings to the corresponding second subset of fields, the removing of the duplicates comprising removing a mapping of at least one string of the first subset of the plurality of strings to an "other" field in the mapping between the first subset of plurality of strings to the corresponding first subset of fields, the at least one string being similar to a second string in the mapping between the second subset of the plurality of strings and the corresponding second subset of fields; and
automatically generating an electronic template of the invoice using the combined mapping.
11. (Currently Amended) A system comprising:
a non-transitory computer readable medium storing computer program instructions; and
at least one processor configured to execute the computer program instructions to cause operations comprising:
[the limitation as in claim 1, above.]
This reads on a human (e.g., mentally and/or using pen and paper):
Extracting characters from a document;
Matching characters to a list of fields/spaces and writing down the characters in a determined field/space;
Using a predetermined set of rules to write down the characters in a second determined field/space;
Combining the written characters in the determined fields/spaces;
Removing duplicates from the written characters in the determined fields/spaces and removing duplicates from the written characters in the determined fields/spaces using similarity information between the written characters in the determined fields/spaces.
Writing down a final template considering the combination above.
Please also refer to MPEP 2106.05(f)(2): Whether the claim invokes computers or other machinery merely as a tool to perform an existing process, and MPEP 2106.06(b): Clear Improvement to a Technology or to Computer Functionality.
Please refer to MPEP 2106.04(2): Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception: Prong Two.
“Prong Two asks does the claim recite additional elements that integrate the judicial exception into a practical application? In Prong Two, examiners evaluate whether the claim as a whole integrates the exception into a practical application of that exception. If the additional elements in the claim integrate the recited exception into a practical application of the exception, then the claim is not directed to the judicial exception (Step 2A: NO) and thus is eligible at Pathway B. This concludes the eligibility analysis. If, however, the additional elements do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception (Step 2A: YES), and requires further analysis under Step 2B (where it may still be eligible if it amounts to an ‘‘inventive concept’’). For more information on how to evaluate whether a judicial exception is integrated into a practical application, see MPEP § 2106.04(d)(2).”
From this analysis, in Step 2A, Prong Two, the Examiner has evaluated the independent claims accordingly and determined that the amended independent claims as drafted that the claims as a whole do not include additional elements that integrate the exception into a practical application of that exception. (i.e., an abstract idea). Similar to what was discussed in the Non-Final Rejection mailed on 10/23/2025:
This judicial exception is not integrated into a practical application because for example: claim 1 recites “a computer implemented method” and “an electronic document” while claim 11 recites “a system”, “a non-transitory computer readable medium”, “computer program instructions”, and “at least one processor”. As an example, in [0039-0040] of the as filed specification, “[0039] …In one or more embodiments, the computing device 800 includes one or more processors 802, one or more input devices 804, one or more display devices 806, one or more network interfaces 808, and one or more computer-readable media 812. Each of these components is be coupled by a bus 810. [0040] Display device 806 includes any display technology, including but not limited to display devices using Liquid Crystal Display (LCD) or Light Emitting Diode (LED) technology. Processor(s) 702 uses any processor technology, including but not limited to graphics processors and multi-core processors. Input device 804 includes any known input device technology, including but not limited to a keyboard (including a virtual keyboard), mouse, track ball, and touch-sensitive pad or display. Bus 810 includes any internal or external bus technology, including but not limited to ISA, EISA, PCI, PCI Express, USB, Serial ATA or FireWire. Computer-readable medium 812 includes any non-transitory computer readable medium that provides instructions to processor(s) 802 for execution, including without limitation, non-volatile storage media (e.g., optical disks, magnetic disks, flash drives, etc.), or volatile media (e.g., SDRAM, ROM, etc.).” Therefore, a general-purpose computer or computing device is described and mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical idea because it does not impose any meaningful limits on practicing the abstract idea.
Please also refer to MPEP 2106.05(f)(2): Whether the claim invokes computers or other machinery merely as a tool to perform an existing process.
Finally, please refer to MPEP 2106.05(A): Relevant Considerations For Evaluating Whether Additional Elements Amount To An Inventive Concept
“Limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception include:
i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));”
From this analysis, in Step 2B, the Examiner has evaluated the independent claims accordingly and determined that the independent claims as drafted have limitations that the courts have found not to be enough to qualify as "significantly more" when recited in a claim with a judicial exception. Similar to what was discussed in the Non-Final Rejection mailed on 10/23/2025:
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is listed as a general computing device as noted. The claim is not patent eligible.
In summary, the Examiner respectfully disagrees with the arguments above. Please refer to analysis above.
For more details, please refer to updated 35 U.S.C. § 101 rejections for claims 1 and 11, below.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. More specifically directed to the abstract idea grouping of: mental process.
The independent claim(s) recite(s):
1. (Original) A computer-implemented method, comprising:
extracting a plurality of strings from an electronic document comprising an invoice;
executing fuzzy matching between the extracted plurality of strings and a list of fields to map a first subset of the plurality of strings to a corresponding first subset of fields;
invoking a trained natural language processing model to map a second subset of the plurality of strings to a corresponding second subset of fields, the natural language processing model being trained by providing training questions, corresponding training context, and corresponding training answers, the invoking comprising providing a question and a corresponding context and receiving a corresponding answer;
combining the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of plurality of strings to the corresponding second subset of fields, the combining of the mapping comprising:
removing duplicates from the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of the plurality of strings to the corresponding second subset of fields, the removing of the duplicates comprising removing a mapping of at least one string of the first subset of the plurality of strings to an "other" field in the mapping between the first subset of plurality of strings to the corresponding first subset of fields, the at least one string being similar to a second string in the mapping between the second subset of the plurality of strings and the corresponding second subset of fields; and
automatically generating an electronic template of the invoice using the combined mapping.
11. (Currently Amended) A system comprising:
a non-transitory computer readable medium storing computer program instructions; and
at least one processor configured to execute the computer program instructions to cause operations comprising:
[the limitation as in claim 1, above.]
This reads on a human (e.g., mentally and/or using pen and paper):
Extracting characters from a document;
Matching characters to a list of fields/spaces and writing down the characters in a determined field/space;
Using a predetermined set of rules to write down the characters in a second determined field/space;
Combining the written characters in the determined fields/spaces;
Removing duplicates from the written characters in the determined fields/spaces and removing duplicates from the written characters in the determined fields/spaces using similarity information between the written characters in the determined fields/spaces.
Writing down a final template considering the combination above.
This judicial exception is not integrated into a practical application because for example: claim 1 recites “a computer implemented method” and “an electronic document” while claim 11 recites “a system”, “a non-transitory computer readable medium”, “computer program instructions”, and “at least one processor”. As an example, in [0039-0040] of the as filed specification, “[0039] …In one or more embodiments, the computing device 800 includes one or more processors 802, one or more input devices 804, one or more display devices 806, one or more network interfaces 808, and one or more computer-readable media 812. Each of these components is be coupled by a bus 810. [0040] Display device 806 includes any display technology, including but not limited to display devices using Liquid Crystal Display (LCD) or Light Emitting Diode (LED) technology. Processor(s) 702 uses any processor technology, including but not limited to graphics processors and multi-core processors. Input device 804 includes any known input device technology, including but not limited to a keyboard (including a virtual keyboard), mouse, track ball, and touch-sensitive pad or display. Bus 810 includes any internal or external bus technology, including but not limited to ISA, EISA, PCI, PCI Express, USB, Serial ATA or FireWire. Computer-readable medium 812 includes any non-transitory computer readable medium that provides instructions to processor(s) 802 for execution, including without limitation, non-volatile storage media (e.g., optical disks, magnetic disks, flash drives, etc.), or volatile media (e.g., SDRAM, ROM, etc.).” Therefore, a general-purpose computer or computing device is described and mainly used as an application thereof. Accordingly, these additional elements do not integrate the abstract idea into a practical idea because it does not impose any meaningful limits on practicing the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of using a computer is listed as a general computing device as noted. The claim is not patent eligible.
With respect to claims 2 and 12, the claim(s) recite:
2 and 12. (Original) The computer-implemented method/system of claims 1 and 11, the extracting of the plurality of strings comprising:
extracting the plurality of strings as hierarchical text blocks.
This reads on a human (e.g., mentally and/or using pen and paper):
Extracting characters in a known predefined order.
No additional limitations are present.
With respect to claims 3 and 13, the claim(s) recite:
3 and 13. (Original) The computer-implemented method/system of claims 1 and 11, the extracting of the plurality of strings comprising;
extracting the plurality of strings and corresponding geometrical information of the plurality of strings within the electronic document.
This reads on a human (e.g., mentally and/or using pen and paper):
Extracting the characters considering geometrical information (e.g., enclosures like squares/rectangles).
No additional limitations are present.
With respect to claims 4 and 14, the claim(s) recite:
4 and 14. (Original) The computer-implemented method/system of claims 1 and 11, the executing the fuzzy matching comprising:
mapping a string of the first subset of the plurality of strings to a corresponding standard field of a computer application.
This reads on a human (e.g., mentally and/or using pen and paper):
Writing down characters in a list of fields/spaces of a predefined form.
No additional limitations are present.
With respect to claims 5 and 15, the claim(s) recite:
5 and 15. (Original) The computer-implemented method/system of claims 1 and 11, the invoking of the trained natural language processing model comprising:
invoking a DistilBERT model trained using an organization specific data comprising the training questions, the corresponding training context, and the corresponding training answers.
This reads on a human (e.g., mentally and/or using pen and paper):
Using predetermined set of rules and organization data to write down the characters in a second determined field/space.
No additional limitations are present.
With respect to claims 7 and 17, the claim(s) recite:
7 and 17. (Original) The computer-implemented method/system of claims 1 and 11, the removing of the duplicates further comprising:
removing duplicate mappings of at least one string of the plurality of strings based on a geometrical location of the at least one string within the electronic document.
This reads on a human (e.g., mentally and/or using pen and paper):
Removing duplicates from the written characters in the determined fields/spaces using location information of the characters.
No additional limitations are present.
With respect to claims 9 and 19, the claim(s) recite:
9 and 19. (Original) The computer-implemented method/system of claims 1 and 11, the removing of the mapping comprising:
removing the mapping of the at least one string in response to a similarity score between the at least one string and the second string being above a threshold.
This reads on a human (e.g., mentally and/or using pen and paper):
Removing duplicates from the written characters in the determined fields/spaces using similarity information/score between the written characters in the determined fields/spaces.
No additional limitations are present.
With respect to claims 10 and 20, the claim(s) recite:
10 and 20. (Original) The computer-implemented method/system of claims 1 and 11, the generating of the electronic template comprising:
generating the electronic template based on geometrical locations of the plurality of strings.
This reads on a human (e.g., mentally and/or using pen and paper):
Writing down a final template considering the combination above as well as the locations of the characters.
No additional limitations are present.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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, 7, 10-11, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyubarskiy et al. (US 20160171627 A1) and further in view of Shen et al. (US 20190354578 A1) and Anand et al. (US 9652790 B2).
As to independent claim 1, Lyubarskiy et al. teaches:
1. (Original) A computer-implemented method (see ¶ [0016]: “Described herein are methods and systems of processing electronic documents for invoice recognition.”), comprising:
extracting a plurality of strings from an electronic document comprising an invoice (see ¶ [0020 and 0038]: “[0020] In some embodiments, elements of an invoice and/or other information related to the invoice can be extracted from an image of the invoice automatically without user interaction... [0038] In some embodiments, application 190 can extract information of one or more invoices and/or provide the information to a user based on a user request.”);
executing fuzzy matching between the extracted plurality of strings and a list of fields to map a first subset of the plurality of strings to a corresponding first subset of fields (see ¶ [0075]: “At block 516, the processing device can compare data fields of the identified records with one or more header elements to identify one or more matches. For example, the processing device can compare data types associated with data fields of an identified record to data types defined by the header elements. The processing device then identifies a match between the record and the header elements upon identifying a data field of the record that is associated with a data type (e.g., “character string”) defined by one of the identified header elements (e.g., “description of goods”)”);
automatically generating an electronic template of the invoice using the (see ¶ [0021]: “Aspects of the present disclosure may perform invoice recognition without knowing a format used by a vendor that generated the invoice, and may use the recognized invoice elements to derive such a format and then utilize it as a template to process invoices of the same vendor or invoices having a similar format…”).
However, Lyubarskiy et al. does not explicitly teach, but Shen et al. does teach:
invoking a trained natural language processing model to map a second subset of the plurality of strings to a corresponding second subset of fields (see ¶ [0007 and 0010-0012]: “[0007] In one embodiment, a system generates annotated natural language phrases. The system receives a phrase that includes at least one tagged object and generates instantiated phrases by instantiations of each tagged object in the phrase. The system generates lists of natural language phrases by corresponding paraphrases of each of the instantiated phrases. The system generates ordered lists of natural language phrases by ordering natural language phrases in each list of natural language phrases based on occurrences of each natural language phrase. The system generates annotated natural language phrases by using each tagged object in the phrase to annotate the ordered lists of natural language phrases. [0010] For example, a server receives a phrase “find @food,” from a software developer, and generates instantiated phrases such as “find a pizza,” “find a taco,” and “find an apple.” The server uses a paraphrase generator to generate: 1) a pizza list of natural language phrases (“order a pizza,” “what restaurant serves pizzas,” “which store sells pizzas,” etc.); 2) a taco list of natural language phrases (“order a taco,” “what restaurant serves tacos,” “which store sells tacos,” etc.); and 3) an apple list of natural language phrases (“order an apple,” “what restaurant serves apples,” “which store sells apples,” etc.) [0011] The server orders these lists based on how often these phrases occur in searches of a natural language model, thereby producing: 1) an ordered pizza list: i) “order a pizza,” ii) “what restaurant serves pizzas,” iii) “which store sells pizzas,” etc.; 2) an ordered taco list: i) “what restaurant serves tacos,” ii) “order a taco,” iii) “which store sells tacos,” etc.; and 3) an ordered apple list: i) “what store has a sale on apples,” ii) “which store sells apples,” iii) “order an apple,” etc. [0012] The server annotates the lists to produce: 1) an annotated pizza list: i) “order a pizza (@food),” ii) “what restaurant serves pizzas (@food),” iii) “which store sells pizzas (@food),” etc.; 2) an annotated taco list: i) “what restaurant serves tacos (@food),” ii) “order a taco (@food),” iii) “which store sells tacos (@food),” etc.; and 3) an annotated apple list: i) “what store has a sale on apples (@food),” ii) “which store sells apples (@food),” iii) “order an apple (@food),” etc.”),
the natural language processing model being trained by providing training questions, corresponding training context, and corresponding training answers (see ¶ [0007 and 0010-0012] citations as in limitation above and further ¶ [0034]: “ … The natural language model 114 model generally refers to a distribution function trained based on, for example online documents, that predicts the next word in a sentence, possibly given the previous word(s), and may be trained on a public corpus, such as Wikipedia®.”
¶ [0043]: “…A developer can enter many different types of phrases into the “user says” window 202 to train the NLU engine 130 to understand many different phrase from a user. For example, a developer may enter “book @flight,” “rent @car,” “reserve @hotel,” and “find @food,” to generate annotated natural language phrases for training the NLU engine 130. Subsequently, the trained NLU engine 130 understands when a user requests to purchase two airline tickets to San Francisco, rent a car at the San Francisco airport, reserve a hotel room in downtown San Francisco, and reserve a table at a romantic restaurant that is not far from the hotel.”
¶ [0086]: “The enhanced set of natural language phrases is annotated for training the NLU engine 130. The system generates annotated natural language phrases by using each tagged object in the phrase to annotate the enhanced set of natural language phrases that is based on the ordered lists of natural language phrases.”),
the invoking comprising providing a question and a corresponding context and receiving a corresponding answer (see ¶ [0007 and 0010-0012, 0034, 0043, and 0086] citations as in limitation(s) above and further ¶ [0091]: “The annotated natural language phrases can be used to train the NLU engine 130. The system can use the annotated natural language phrases to train a NLU engine to understand a natural language phrase from a user, thereby enabling a response. For example, the trainer 126 uses the annotated natural language phrases 336 to train the NLU engine 130, and the natural language server 106 provides a copy of the personal digital assistant 128 to smartphone 102. Continuing the example, a person uses smartphone 102 and says the phrase “order delivery of a pizza.” The NLU engine 130 understands the phrase, which helps the personal digital assistant 128 prompt the person for details, such as pizza size and toppings, the restaurant to make the pizza, and delivery time and location. The personal digital assistant 128 correctly completes the person's pizza delivery order because the trainer 126 used the annotated natural language phrases 336 to train the NLU engine 130.”);
combining the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of plurality of strings to the corresponding second subset of fields (see ¶ [0073-0076]: “[0073] The organizer 132 can use fuzzy matching to match tagged natural language phrases that are not exact matches, such as a fuzzy match between the tagged natural language phrases “what store has a sale on @food” and the tagged natural language phrases “what store has sales on @food.” The resulting merged list of tagged natural language phrases 328 is “order delivery of @food,” “where is a restaurant that serves @food,” “which store has a sale on @food,” “where is a store that sells @food.” Although FIG. 3A depicts only four tagged natural language phrases in the merged list for the purposes of simplifying the examples, the merged list can include any number of tagged natural language phrases. [0074] The tagged natural language phrases are ordered within the merged list of tagged natural language phrases based on how often their natural language phrases occur. The system generates an ordered list of tagged natural language phrases by ordering the merged list of tagged natural language phrases based on occurrences of each corresponding natural language phrase. For example, the organizer 132 counts 29 occurrences of “order delivery of a pizza,” 13 occurrences of “order delivery of a taco,” and 3 occurrences of “order delivery of an apple” in the natural language model 122, such that the total count is 45 for the merged phrase “order delivery of @food.” [0075] In another example, the organizer 132 counts 17 occurrences of “where is a restaurant that serves pizzas” and 23 occurrences of “where is a restaurant that serves tacos” in the natural language model 122, such that the total count is 40 for the merged phrase “where is a restaurant that serves @food.” [0076] In yet another example, the organizer 132 counts 7 occurrences of “which store has a sale on pizzas,” 5 occurrences of “which store has a sale on tacos,” and 19 occurrences of “which store has a sale on apples” in the natural language model 122, such that the total count is 33 for the merged phrase “which store has a sale on @food.” In a further example, the organizer 132 counts 11 occurrences of “where is a store that sells apples” in the natural language model 122, such that the total count is 11 for the merged phrase “where is a store that sells @food.””); and
automatically generating an electronic template (see ¶ [0074]: “…The system generates an ordered list of tagged natural language phrases by ordering the merged list of tagged natural language phrases based on occurrences of each corresponding natural language phrase. For example, the organizer 132 counts 29 occurrences of “order delivery of a pizza,” 13 occurrences of “order delivery of a taco,” and 3 occurrences of “order delivery of an apple” in the natural language model 122, such that the total count is 45 for the merged phrase “order delivery of @food.””).
Lyubarskiy et al. and Shen et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in text/template generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lyubarskiy et al. to incorporate the teachings of Shen et al. of invoking a trained natural language processing model to map a second subset of the plurality of strings to a corresponding second subset of fields, the natural language processing model being trained by providing training questions, corresponding training context, and corresponding training answers, the invoking comprising providing a question and a corresponding context and receiving a corresponding answer; combining the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of plurality of strings to the corresponding second subset of fields; and automatically generating an electronic template using the combined mapping which provides the benefit of generating an enhanced set of natural language phrases ([0047] of Shen et al.).
However, Lyubarskiy et al. in combination with Shen et al. does not explicitly teach, but Anand et al. does teach:
the combining of the mapping comprising: removing duplicates from the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of the plurality of strings to the corresponding second subset of fields (see ¶ at Col. 9, line 28 – Col. 10, line 6: “(30) At 506, the first set of mapped data may be augmented with additional data imported into the shared data management system from other data locations to create an augmented set of mapped data. SDM provides a common and comprehensive data model which encompasses and consolidates individual data models used by different applications. The development of this consolidated SDM data model may be incrementally performed by adding data models of applications one by one. Once the first application's attributes are added to the SDM data model, the second application's attributes not covered by the first application may be added (augmented) to the SDM model. (31) At 508, one or more of second set of mapped data may be received from one or more shared data management system associated respectively with one or more shared data management networks. (32) At 510, a data set may be composed by combining and cleansing one or more of said first set of mapped data, said second set of mapped data, and said augmented set of mapped data. A data set refers to a data model of an application. The SDM data model is a comprehensive model combining the models of many applications. Often this composing process requires human intervention due to its semantic-heavy nature which cannot be easily automated by machine, for example, to remove duplicated (same meaning) attributes with different names from different applications or keeping different attributes with similar or same names from different applications. This process is referred to as composing. Cleansing includes removing duplicated (same meaning) attributes with different names from different applications or keeping different attributes with similar or same names from different applications. At 512, the composed data set may be offered in a digital marketplace with associated pricing characteristics. (33) In one aspect, SMD of the present disclosure may identify a quality measure based on voting or certification or both, the quality measure associated with characteristics of authenticity, reliability, and accuracy of database associated with said other data locations or the plurality of applications contributing to said data set. SDM in one embodiment of the present disclosure may also quantify value of the data set based on characteristics associated with availability of data forming the data set, difficulty in procuring the data and potential use of the augmented and composed data set. A pricing to the data set may be automatically assigned based on the quantified value and the quality measures.”enable users to view and explore the data, provide security, access control, data import, data access and export, transaction and analysis capabilities),
the removing of the duplicates comprising removing a mapping of at least one string of the first subset of the plurality of strings to an "other" field in the mapping between the first subset of plurality of strings to the corresponding first subset of fields (see ¶ at Col. 9, line 28 – Col. 10, line 6 citations as in limitations above. More specifically: “(32) …Taking the illustrated empty digital form in FIG. 2 as an example, Segment A on page 1, which includes Fields c and d, and Segment J on page 2, which includes Fields h and i, both map to a form schema type X. Segment B on page 1, which includes Fields e, f, and g, and Segment D on page 2, which also includes Fields e, f, and g, both map to a form schema type Y. Accordingly, schema mapping engine 102 identifies Segment A, Segment B, Segment D, and Segment J as schema mapped segments in the empty digital form…” and “(34) Merge and filter engine 106 is configured to merge the outputs from schema mapping engine 102 and repeatability detection engine 104, remove duplicates, and generate a collection of one or more groups of potentially linkable segments in an empty digital form…”),
the at least one string being similar to a second string in the mapping between the second subset of the plurality of strings and the corresponding second subset of fields (see ¶ at Col. 9, line 28 – Col. 10, line 6 citations as in limitations above. ).
Lyubarskiy et al. and Shen et al. and Anand et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in text/template generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lyubarskiy et al. and Shen et al. to incorporate the teachings of Anand et al. of the combining of the mapping comprising: removing duplicates from the mapping of the first subset of the plurality of strings to the corresponding first subset of fields and the mapping of the second subset of the plurality of strings to the corresponding second subset of fields, the removing of the duplicates comprising removing a mapping of at least one string of the first subset of the plurality of strings to an "other" field in the mapping between the first subset of plurality of strings to the corresponding first subset of fields, the at least one string being similar to a second string in the mapping between the second subset of the plurality of strings and the corresponding second subset of fields which provides the benefit of enabling users to view and explore the data, provide security, access control, data import, data access and export, transaction and analysis capabilities (¶ at Col. 3, lines 51 onward of Anand et al.).
As to independent claim 11, Lyubarskiy et al. teaches:
11. (Currently Amended) A system comprising:
a non-transitory computer readable medium storing computer program instructions (see ¶ [0105]: “The data storage device 718 may include a machine-accessible storage medium 724 on which is stored software 726 embodying any one or more of the methodologies of functions described herein. The software 726 may also reside, completely or at least partially, within the main memory 704 as instructions 726 and/or within the processing device 702 as processing logic 726 during execution thereof by the computer system 700; the main memory 704 and the processing device 702 also constituting machine-accessible storage media.” and Claim 22: “22. A non-transitory machine-readable storage medium including instructions that, when accessed by a processing device, cause the processing device to perform operations…”); and
at least one processor configured to execute the computer program instructions to cause operations (see ¶ [0105] and Claim 22 citations as in limitation above.) comprising:
[the limitation as in claim 1, taught by Lyubarskiy et al. and Shen et al., above.]
Regarding claims 7 and 17, Lyubarskiy et al. in combination with Shen et al. and Anand et al. teaches all of the limitations as in claims 1 and 11, above.
Anand et al. further teaches:
7 and 17. (Original) The computer-implemented method/system of claims 1 and 11, the removing of the duplicates (see ¶ at Col. 9, line 28 – Col. 10, line 6 citations as in claims 1 and 11, above.) further comprising:
removing duplicate mappings of at least one string of the plurality of strings based on a geometrical location of the at least one string within the electronic document (see ¶ at Col. 9, line 28 – Col. 10, line 6 citations as in claims 1 and 11, above.).
Lyubarskiy et al. and Shen et al. and Anand et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in text/template generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lyubarskiy et al. and Shen et al. to incorporate the teachings of Anand et al. of removing duplicate mappings of at least one string of the plurality of strings based on a geometrical location of the at least one string within the electronic document which provides the benefit of enabling users to view and explore the data, provide security, access control, data import, data access and export, transaction and analysis capabilities (¶ at Col. 3, lines 51 onward of Anand et al.).
Regarding claims 10 and 20, Lyubarskiy et al. in combination with Shen et al. and Anand et al. teaches all of the limitations as in claims 1 and 11, above.
10 and 20. (Original) The computer-implemented method/system of claims 1 and 11, the generating of the electronic template (see ¶ [0021]: “Aspects of the present disclosure may perform invoice recognition without knowing a format used by a vendor that generated the invoice, and may use the recognized invoice elements to derive such a format and then utilize it as a template to process invoices of the same vendor or invoices having a similar format…”) comprising:
generating the electronic template based on geometrical locations of the plurality of strings (see ¶ [0021] citation as cited above and further ¶ [0063]: “…For example, the processing device can compare a location of the pivot element and a location of a given preliminary header element and determine whether the position of the preliminary header element is above the position of the pivot element and is aligned with the position of the pivot element….”. ).
Claims 2-3 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyubarskiy et al. (US 20160171627 A1) and further in view of Shen et al. (US 20190354578 A1) and Anand et al. (US 9652790 B2) as applied to claims 1 and 11 above, and further in view of Blackman et al. (US 20220188885 A1).
Regarding claims 2 and 12, Lyubarskiy et al. in combination with Shen et al. and Anand et al. teaches all of the limitations as in claims 1 and 11, above.
Lyubarskiy et al. further teaches:
2 and 12. (Original) The computer-implemented method/system of claims 1 and 11, the extracting of the plurality of strings (see ¶ 0020 and 0038] citations as in claims 1 and 11, above.) comprising:
However, Lyubarskiy et al. in combination with Shen et al. and Anand et al. does not explicitly teach, but Blackman et al. does teach:
extracting the plurality of strings as hierarchical text blocks (see ¶ [0093]: “…As described herein above, the one or more objects can be text objects that stored in a hierarchical format, such as a JSON format. For example, each page in the processed invoice file can be represented as a block object, which can contain one or more line objects containing one or more word objects. The output of a Textract process creates a box (e.g., an identified region of the file 275) with coordinates around each string of unbroken text within the file 275 (e.g., as one or more line or word objects in a hierarchy). These objects are provided as output in a JSON data structure, which includes the pieces of encompassed text and a confidence rating (e.g., which indicates a relative confidence that the text recognition is accurate for the particular segment of text). The objects can include data structures that include pointers or identifiers to other data structures lower in the hierarchy. A block can contain a list of pointers to line objects, and each line object can include a list of points to word objects, which contain the text information. The word objects can include text information for a single extracted word in an encoded format, for example ASCII or UNICODE…”).
Lyubarskiy et al. and Shen et al. and Blackman et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in text/template generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lyubarskiy et al. in combination with Shen et al. and Anand et al. to incorporate the teachings of Blackman et al. of extracting the plurality of strings as hierarchical text blocks which provides the benefit of technical improvements to invoice analysis systems ([0003] of Blackman et al.).
Regarding claims 3 and 13, Lyubarskiy et al. in combination with Shen et al. and Anand et al. teaches all of the limitations as in claims 1 and 11, above.
Lyubarskiy et al. further teaches:
3 and 13. (Original) The computer-implemented method/system of claims 1 and 11, the extracting of the plurality of strings comprising (see ¶ 0020 and 0038] citations as in claims 1 and 11, above.);
However, Lyubarskiy et al. in combination with Shen et al. and Anand et al. does not explicitly teach, but Blackman et al. does teach:
extracting the plurality of strings and corresponding geometrical information of the plurality of strings within the electronic document (see ¶ [0093] citation as in claims 3 and 13, above.).
Lyubarskiy et al. and Shen et al. and Blackman et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in text/template generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lyubarskiy et al. in combination with Shen et al. and Anand et al. to incorporate the teachings of Blackman et al. of extracting the plurality of strings and corresponding geometrical information of the plurality of strings within the electronic document which provides the benefit of technical improvements to invoice analysis systems ([0003] of Blackman et al.).
Claims 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyubarskiy et al. (US 20160171627 A1) and further in view of Shen et al. (US 20190354578 A1) and Anand et al. (US 9652790 B2) as applied to claims 1 and 11 above, and further in view of Chao et al. (US 11960827 B1).
Regarding claims 4 and 14, Lyubarskiy et al. in combination with Shen et al. and Anand et al. teaches all of the limitations as in claims 1 and 11, above.
Lyubarskiy et al. further teaches:
4 and 14. (Original) The computer-implemented method/system of claims 1 and 11, the executing the fuzzy matching (see ¶ [0075] citation as in claims 1 and 11, above.) comprising:
However, Lyubarskiy et al. in combination with Shen et al. and Anand et al. does not explicitly teach, but Chao et al. does teach:
mapping a string of the first subset of the plurality of strings to a corresponding standard field of a computer application (see ¶ Col. 6, lines 29-38: “(36) The messager application 218 may include a searcher 222. The searcher 222 may search for text strings 160, 164 matching content types to fill out fields 108, 112 of a webpage. The searcher 222 may search for the text strings 160, 164 using any of the methods, functions, or techniques described with respect to the searcher 210, such as text strings proximal to, adjacent to, and/or preceding text that is similar to the content type, or text strings in predetermined locations in the messages based on URLs and/or domain names associated with the webpage.”).
Lyubarskiy et al. and Shen et al. and Chao et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in text/template generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lyubarskiy et al. and Shen et al. to incorporate the teachings of Chao et al. of mapping a string of the first subset of the plurality of strings to a corresponding standard field of a computer application which provides the benefit of saving the user time, and saving computing resources (¶ at Col. 2, lines 66 of Chao et al.).
Claims 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyubarskiy et al. (US 20160171627 A1) and further in view of Shen et al. (US 20190354578 A1) and Anand et al. (US 9652790 B2) as applied to claims 1 and 11 above, and further in view of Kung (US 12361138 B1).
Regarding claims 5 and 15, Lyubarskiy et al. in combination with Shen et al. and Anand et al. teaches all of the limitations as in claims 1 and 11, above.
Shen et al. further teaches:
5 and 15. (Original) The computer-implemented method/system of claims 1 and 11, the invoking of the trained natural language processing model (see ¶ [0007 and 0010-0012] citations as in claims 1 and 11, above.) comprising:
an organization specific data comprising the training questions, the corresponding training context, and the corresponding training answers (see ¶ [0007 and 0010-0012] citations as claims 1 and 11 above and further ¶ [0034]: “ … The natural language model 114 model generally refers to a distribution function trained based on, for example online documents, that predicts the next word in a sentence, possibly given the previous word(s), and may be trained on a public corpus, such as Wikipedia®.”
¶ [0043]: “…A developer can enter many different types of phrases into the “user says” window 202 to train the NLU engine 130 to understand many different phrase from a user. For example, a developer may enter “book @flight,” “rent @car,” “reserve @hotel,” and “find @food,” to generate annotated natural language phrases for training the NLU engine 130. Subsequently, the trained NLU engine 130 understands when a user requests to purchase two airline tickets to San Francisco, rent a car at the San Francisco airport, reserve a hotel room in downtown San Francisco, and reserve a table at a romantic restaurant that is not far from the hotel.”
¶ [0086]: “The enhanced set of natural language phrases is annotated for training the NLU engine 130. The system generates annotated natural language phrases by using each tagged object in the phrase to annotate the enhanced set of natural language phrases that is based on the ordered lists of natural language phrases.”)
Lyubarskiy et al. and Shen et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in text/template generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lyubarskiy et al. to incorporate the teachings of Shen et al. of the invoking of the trained natural language processing model and an organization specific data comprising the training questions, the corresponding training context, and the corresponding training answers which provides the benefit of generating an enhanced set of natural language phrases ([0047] of Shen et al.).
However, Lyubarskiy et al. in combination with Shen et al. and Anand et al. does not explicitly teach, but Kung does teach:
invoking a DistilBERT model trained using an organization specific data (see ¶ at Col. 5, line 54 – Col. 6, line 2: “(27) Generally, a pre-trained model has been trained using a relatively large training dataset to perform a task that is similar or related to a downstream task. In embodiments of the invention, the pre-trained model has been trained for natural language processing tasks. Examples of such pre-trained models include the BERT, ROBERTa, and DistilBERT models. A pre-trained model may be fine-tuned to perform a particular a natural language processing task. Fine-tuning entails training the pre-trained model for the objective of the downstream task using a relatively small training dataset, compared to that used to pre-train the model. A pre-trained BERT, ROBERTa, DistilBERT, or other transformer model may be fine-tuned to perform sequence, span, or text classification as described herein. Creation of training datasets for fine-tuning a pre-trained model is later described beginning with FIG. 3.”).
Lyubarskiy et al. and Shen et al. and Kung et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in text/template generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lyubarskiy et al. and Shen et al. to incorporate the teachings of Kung of invoking a DistilBERT model trained using an organization specific data which provides the benefit of allowing for identification of application names with excellent accuracy (¶ at Col. 4, lines 40-50 of Kung).
Claims 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lyubarskiy et al. (US 20160171627 A1) and further in view of Shen et al. (US 20190354578 A1) and Anand et al. (US 9652790 B2) as applied to claims 1 and 11 above, and further in view of Jain et al. (US 10482171 B2).
Regarding claims 9 and 19, Lyubarskiy et al. in combination with Shen et al. and Anand et al. teaches all of the limitations as in claims 8 and 18, above.
Anand et al. further teaches:
9 and 19. (Original) The computer-implemented method/system of claims 8 and 18, the removing of the mapping (see ¶ at Col. 9, line 28 – Col. 10, line 6 citations as in claims 8 and 18, above.) comprising:
However, Lyubarskiy et al. in combination with Shen et al. and Anand et al. does not explicitly teach, but Jain et al. does teach:
removing the mapping of the at least one string in response to a similarity score between the at least one string and the second string being above a threshold (see ¶ Col. 9, line 56 – Col. 10, line 35: “For each group of potentially linkable segments, link type deduction engine 108 can start with a child segment in the group of potentially linkable segments, and determine a type of link, if any, to create for the child segment. The link created for the child segment may or may not be to its parent segment. In some embodiments, link type deduction engine 108 is configured to determine a type of link, if any, to create for a child segment in a group of potentially linkable segments based on a similarity metric of the child segment to its parent segment. The similarity metric is determined from a comparison of the contents of the child segment and the contents of the parent segment in a corpus of existing completed forms of the same type as the empty digital form. The corpus of existing completed forms may be maintained in a form data repository 114. For example, the similarity may be expressed as
similarity=[Σ.sub.i=1:NI(value(child segment) == value (parent segment))] / N [1]
where I( ) is the identity function, value( ) is the contents of the segment, and N is the number of completed forms of the same type as the empty digital form. A similarity value of 1 implies that, over the corpus of consumer filled data in the completed forms of the same type as the empty digital form, the contents of the child segment are the same as the contents of the parent segment. As a result, the child segment is a redundant segment that is not necessary to the meaning or function of the digital form. That is, the child segment need not be completed by a consumer during the form filling experience. In this instance, link type deduction engine 108 can determine (conclude) that a hard link to the parent segment can be created for the child segment. In contrast, a similarity value not equal to 1 but satisfying a similarity threshold (similarity threshold value) implies that, over the corpus of user filled data in the completed forms of the same type as the empty digital form, the contents of the child segment are the same as the contents of the parent segment for at least the similarity threshold of the corpus of completed forms. Depending on the value set for the similarity threshold (e.g., the similarity threshold is set to a sufficiently large value), the contents of the child segment are the same as the contents of the parent segment for a majority or sufficient number of the corpus of completed forms.”).
Lyubarskiy et al. and Shen et al. and Anand et al. and Jain et al. are considered to be analogous to the claimed invention because they are in the same field of endeavor in text/template generation. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Lyubarskiy et al. and Shen et al. and Anand et al. to incorporate the teachings of Jain et al. of removing the mapping of the at least one string in response to a similarity score between the at least one string and the second string being above a threshold which provides the benefit of optimizing digital forms (¶ at Col. 1, lines 5-7 onward of Jain et al.).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Keisha Y Castillo-Torres whose telephone number is (571)272-3975. The examiner can normally be reached Monday - Friday, 9:00 am - 4:00 pm (EST).
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Keisha Y. Castillo-Torres
Examiner
Art Unit 2659
/Keisha Y. Castillo-Torres/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659