DETAILED ACTION
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-3, 6-10, 13-17 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 9-10, 12 and 20 of U.S. Patent No.12/039,798. Although the claims at issue are not identical, they are not patentably distinct from each other because
Current application 18/746,820
Parent Application now PN 12/039,798
Regarding claims 1, 8 and 15, Gao discloses a method, apparatus and a non-transitory computer-readable medium, hereinafter referenced as a method for data processing, comprising:
one or more memories storing processor-executable code; and one or more processors coupled with the one or more memories and individually or collectively operable to execute the code comprising:
receiving a query input comprising an input document and a natural language query, the input document including a plurality of input text fields, wherein at least one input text field comprises a key-value pair at a two-dimensional location within the input document, and wherein the natural language query indicates a request for a value associated with a key in the input document; identifying, based at least in part on the query input, an input key phrase that corresponds with the key associated with the natural language query of the query input, the input key phrase requesting the value associated with the key indicated via the natural language query; and selecting, using a machine learned model, a character string at a two-dimensional location in the input document as the value of the key that corresponds with the input key phrase, wherein the machine learned model is trained using a plurality of two-dimensional locations in the input document corresponding to the input key phrase.
Regarding claims 1, 12 and 20, Gao discloses a method, apparatus and a non-transitory computer-readable medium, hereinafter referenced as a method for data processing, comprising: receiving an input document including a plurality of input text fields; receiving an input key phrase querying a value for a key-value pair that corresponds to one or more of the plurality of input text fields; extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document; inputting the extracted set of character strings, the set of two-dimensional locations, and the input key phrase into a machine learned model that is trained to compute a set of probabilities for the set of character strings corresponding to the value for the key-value pair corresponding to the input key phrase; identifying that a character string of the set of character strings corresponds to the value for the key-value pair corresponding to the input key phrase based at least in part on the inputting and on a respective probability for the character string being the value corresponding to the input key phrase being greater than one or more other respective probabilities of one or more other character strings of the set of character strings; and transmitting the identified value corresponding to the input key phrase.
Regarding claims 2, 9 and 16, Gao discloses a method further comprising:
identifying the key-value pair at the two-dimensional location within the input document based at least in part on an optical character recognition process.
Regarding claims 1, 12 and 20, Gao discloses a method, comprising:
extracting, using an optical character recognition model, a set of character strings and a set of two-dimensional locations of the set of character strings on a layout of the input document.
Regarding claims 3, 10 and 17, Gao discloses a method wherein selecting the character string comprises: extracting the two-dimensional location in the input document based at least in part on the character string being associated with the value of the key that corresponds with the input key phrase (claims 1, 12 and 20).
Regarding claims 1, 12 and 20, Gao discloses a method comprising: inputting the extracted set of character strings, the set of two-dimensional locations, and the input key phrase into a machine learned model that is trained to compute a set of probabilities for the set of character strings corresponding to the value for the key-value pair corresponding to the input key phrase;
Regarding claims 6, 13 and 20, Gao discloses a method further comprising: training the machine learned model based at least in part on inputting a plurality of input file formats into the machine learned model (claim 9).
Regarding claim 9, Gao discloses a method further comprising: training the machine learned model based at least in part on inputting a plurality of input file formats into the machine learned model.
Regarding claims 7 and 14, Gao discloses a method wherein the input document comprises a fixed form, a non-fixed form, or both.
Regarding claim 10, Gao discloses a method wherein the input document comprises a fixed form, a non-fixed form, or both.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claims are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claims are directed to the abstract idea of processing data, as explained in detail below.
The limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a machine learning model” nothing in the claim element precludes the steps from practically being performed by mental processing, organizing human activity and mathematical concepts. For example, the language, receiving a query input comprising an input document and a natural language query, the input document including a plurality of input text fields, wherein at least one input text field comprises a key-value pair at a two-dimensional location within the input document, and wherein the natural language query indicates a request for a value associated with a key in the input document (can be done by a user receiving a natural language input); identifying, based at least in part on the query input, an input key phrase that corresponds with the key associated with the natural language query of the query input, the input key phrase requesting the value associated with the key indicated via the natural language query (can be done by a user identifying a key phrase); and selecting a character string at a two-dimensional location in the input document as the value of the key that corresponds with the input key phrase (can be done by a user selecting a string).
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements which are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
The claims fall under the mental processing category since a human could read a form, interpret a question and find the corresponding field. The claims can be read under organizing human activity since the claims can be read on parsing a document and extracting key-value information. The machine learning aspect can be read on mathematical concepts.
According to Step 1, it includes determining whether the claims fall within a statutory category. The claims include a method, therefore the claims fall within a statutory category. Step 2A Prong one, includes evaluating whether the claims recite a judicial exception. The claims recite a judicial exception, therefore an evaluation is done to determine if the claims fit into one of the categories. As explained, the claims fit into the mental processing concept. Prong 2B is used to evaluate whether the claims recite additional elements that integrate the exception into a practical application. As explained the judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements which are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, the claims are non-statutory.
Although the claims teach a machine learning model that is trained, these elements are conceptual design choices implemented on generic computing devices and generic AI services. The claims do not recite a novel hardware architecture, a new AI model training technique or a unique algorithm that improves the underlying technology.
The dependent claims recite similar language, such as identifying, extracting and generating data, which is all part of the mental processing/organizing human activity category and is non-statutory.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 6-10, 13-17 and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Xu et al. (PGPUB 2022/0309549), hereinafter referenced as a Xu.
Regarding claims 1, 8 and 15, Xu discloses a method, apparatus and a non-transitory computer-readable medium, hereinafter referenced as a method for data processing, comprising:
one or more memories storing processor-executable code (memory; p. 0027); and
one or more processors coupled with the one or more memories and individually or collectively operable (one or computers; p. 0027) to execute the code comprising:
receiving a query input (identify the phrase) comprising an input document (document) and a natural language query, the input document including a plurality of input text fields (date, time, invoice number, etc.; fig. 2D), wherein at least one input text field comprises a key-value pair at a two-dimensional location (two-dimensional; p. 0047) within the input document, and wherein the natural language query indicates a request for a value associated with a key in the input document (fig. 2D with p. 0055);
identifying, based at least in part on the query input, an input key phrase (input text) that corresponds with the key associated with the natural language query of the query input (key-pair value), the input key phrase requesting the value associated with the key indicated via the natural language query (fig. 3 with p. 0055-0076); and
selecting, using a machine learned model, a character string at a two-dimensional location in the input document as the value of the key that corresponds with the input key phrase (two-dimensional; p. 0047), wherein the machine learned model is trained using a plurality of two-dimensional locations in the input document corresponding to the input key phrase (training; p. 0050-0055).
Regarding claims 2, 9 and 16, Xu discloses a method further comprising:
identifying the key-value pair at the two-dimensional location within the input document based at least in part on an optical character recognition process (OCR; p. 0016, 0045, 0075).
Regarding claims 3, 10 and 17, Xu discloses a method wherein selecting the character string comprises:
extracting the two-dimensional location (two-dimensional) in the input document based at least in part on the character string being associated with the value of the key that corresponds with the input key phrase (key; p. 0047 with p. 0055-0054).
Regarding claims 6, 13 and 20, Xu discloses a method further comprising:
training the machine learned model based at least in part on inputting a plurality of input file formats into the machine learned model (machine learning model; p. 0032, 0050-0054).
Regarding claims 7 and 14, Xu discloses a method wherein the input document comprises a fixed form, a non-fixed form, or both (structured/unstructured documents; p. 0003, 0044, 0071).
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.
Claim(s) 4-5, 11-12 and 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu in view of Yue et al. (PGPUB 2014/0214416), hereinafter referenced as Yue.
Regarding claims 4, 11 and 18, Xu discloses a method as described above, but does not specifically teach a method further comprising:
generating a probability score for the character string associated with the value of the key that corresponds with the input key phrase, wherein selecting the character string is based at least in part on the probability score of the character string.
Yue discloses a method comprising generating a probability score (probability value) for the character string associated with the value of the key that corresponds with the input key phrase (character string associated with a command word), wherein selecting the character string is based at least in part on the probability score of the character string (selecting the highest; p. 0046-0048), to assist with recognizing key data.
Therefore, it would have been obvious to one of ordinary skill of the art, before the effective filing date of the claimed invention, to modify the method as described above, to optimize results.
Regarding claims 5, 12 and 19, it is interpreted and rejected for similar reasons as set forth above. In addition, Yue discloses a method wherein selecting the character string is based at least in part on the probability score for the character string and a ranking of the probability score for the character string (ranked in order of probability value; p 0046-0048).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. This information has been detailed in the PTO 892 attached (Notice of References Cited).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAKIEDA R JACKSON whose telephone number is (571)272-7619. The examiner can normally be reached Mon - Fri 6:30a-2:30p.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Daniel Washburn can be reached at 571.272.5551. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAKIEDA R JACKSON/Primary Examiner, Art Unit 2657
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