Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Introduction
This office action is in response to Applicant’s response to submission filed on 12/30/2024. Claims 1-20 are pending of which claims 1, 10 and 19 are independent. As such, claims 1-20 have been examined.
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 1-2, 7-9, 10-11 and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method that, under the broadest reasonable interpretation, claims limitations that cover performance of the limitations in the human mind with the assistance of physical aids (e.g., pen and paper), but for the recitation of generic or well-known or conventional computer components. That is, other than the use of “one or more processors, data extraction program, confidence algorithm”, nothing in these claim limitations precludes the steps from practically being performed in the mind. As a whole, claim 1 pertains to evaluation and determin extraction quality, which is a mental process that a human can do. Individually, each of the limitations also pertains to a mental process and/or insignificant extra solution activity, for example:
receiving, by one or more processors, data corresponding to a data extraction program, the data being associated with at least a first file; (e.g., looking print out of a document or viewing a document or text on a screen.)
executing, by the one or more processors, a confidence algorithm that causes the one or more processors to perform operations comprising: generating a dictionary of words from one or more fields in the first file, wherein an entry in the dictionary of words corresponding to a first word in a first field of the first file includes a first confidence score associated with the first word, (e.g., analyzing the text on the print out or on the screen to determine the legibility (how clearly is the text written), jot down a list of words and score each of them on how well or clearly or visible it is written.)
determining, based at least in part on the first confidence score from the entry in the dictionary of words, a content confidence score corresponding to the first field, (e.g., evaluating the words in a field or box in the printout or on the screen, how clearly is the text written, what is the quality of the handwriting for example, provide a content confidence score.)
and determining, based on the content confidence score and a position confidence score, a certainty score for the first field, the position confidence score being associated with a position of the first field within the first file; (e.g., based on the quality of the text and the position or location of the text, is the text located inside a field like it should be, or written outside the field, come up with a certainty score which is a combination of the content confidence score and position confidence score, how visible is the content and is it located in the correct place.)
determining, by the one or more processors based at least in part on the certainty score, a certainty quotient for the first file indicating a reliability of the data corresponding to the data extraction program; (e.g., averaging the field certain scores to get a reliability or quality score for the whole text file.)
and generating, by the one or more processors, a data object that indicates the certainty quotient. (e.g., provide an overall grade or percentage for the text file.)
The judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving, determining, or outputting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 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 limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 1 is not patent eligible.
The examiner further notes that the use of generic computer components (“one or more processors, data extraction program, confidence algorithm”) to obtain, extract, and/or generate data invokes such generic computer components “merely as a tool to perform an existing process”. MPEP 2106.05(f). MPEP 2106.05(f) further explains:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 1 recite the use of generic computer components (“one or more processors, data extraction program, confidence algorithm”), with respect to performing tasks. MPEP 2106.05(d) and (f) further provides examples of court decisions where the courts found generic computing components to be mere instructions to apply a judicial exception, and further explains “increased speed” (e.g., using a computer to increase the speed of an otherwise mental process) does not provide an inventive concept. For example:
A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) (emphasis added).
Performing repetitive calculations. Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
Claim 10 recites a system claim that corresponds to the method of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 10 further recites “one or more processors; and at least one memory storing processor-executable instructions”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 10 is not patent eligible.
Claim 19 recites a non-transitory computer-readable media claim that corresponds to the method of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 19 further recites “non-transitory computer-readable media storing processor-executable instructions”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 19 is not patent eligible.
Claims 2, 7-9, 11, 16-18, and 20 depend from independent claims 1, 10 and 19 respectively, do not remedy any of the deficiencies of claims 1 and 10, and therefore are rejected on the same grounds as claim 1, 10 and 19 from above.
Claim 2 further recite: wherein generating the dictionary of words further comprises: determining, by the one or more processors, a span value and an offset value of the first word, wherein the entry in the dictionary of words includes (i) the span value, (ii) the offset value, (e.g., looking at words on a document or page, count how many characters for each word and how many characters in the text before the word starts.)
and (iii) the first confidence score; (e.g., determine a confidence score or quality score.)
and the computer-implemented method further comprises: generating, by the one or more processors based on span values and offset values indicated in the dictionary of words, a word confidence score array indicating respective word confidence scores for words present in the first field, and determining, by the one or more processors based on the word confidence score array, the content confidence score. (e.g., determine a confidence score for each word in the dictionary, and come up with an avg score for the text.)
Claim 7 further comprising: determining, by the one or more processors, that at least the certainty score causes the certainty quotient to fail to meet or exceed a certainty threshold; (e.g., look at the score and the math formula to determine if a number in the calculation cause it to fail or exceed a threshold.)
and generating, by the one or more processors, the data object to indicate (i) the certainty quotient and (ii) a representation of the first field of the first file. (e.g., write down note regarding the quality score and copy a description of the data field.)
Claim 8 further recites: determining, by the one or more processors, the content confidence score and the position confidence score based on metadata included as part of the data corresponding to the data extraction program. (e.g., look at the metadata, analyze the quality of the extracted text or data fields and look at the positioning of the text and the data field to determine if the spot and text are in the right place and assign a confidence score or a position score based on rules)
Claim 9 further recites: determining, based on the certainty quotient, a file certainty threshold for a file type associated with the first file, the file certainty threshold indicating a minimum reliability value for data extracted from files of the file type; receiving, by the one or more processors, data associated with a second file of the file type; determining, by the one or more processors, a second certainty quotient corresponding to the second file; determining, by the one or more processors, whether the second certainty quotient satisfies the file certainty threshold; and responsive to determining that the second certainty quotient fails to meet or exceed the file certainty threshold, adjusting, by the one or more processors based on the second certainty quotient, the file certainty threshold. (e.g., determining a minimum acceptable confidence or quality level or threshold based on past experience in similar type of text or documents, then looks at a new or recent text/document, evaluate the clarity or visual readability of the text/document and assign a quality or confidence score, compare the score to determine if they pass the determined threshold, and if the quality of the text/document fails to meet the minimum accepted level, adjust the quality minimum quality standard ore threshold based on that text/document type.)
Claims 11, 16-18 recites feature similar to the claims 2, 7-9 and similar analysis can be applied to these claims respectively.
Claim 20 recites feature similar to claim 2 and similar analysis can be applied to claim 20.
In sum, claims 2, 7-9, 11, 16-18, and 20 depend from claims 1, 10, and 19 respectively, and further recite mental processes as explained above. None of the additional limitations recited in claims 2, 7-9, 11, 16-18, and 20 amount to anything more than the same or a similar abstract idea as recited in claims 1, 10 and 19 respectively. Nor do any limitations in claims 2, 7-9, 11, 16-18, and 20: (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception because the additional limitations of using generic computer components amounts to no more than mere instructions to apply the exception using generic computer components. Claims 2, 7-9, 11, 16-18, and 20 are not patent eligible.
Patent edibility claims
Claims 3-6 and 12-15 are deemed to be patent eligible because they contain sufficient technical details (as human do not practically adjust or fine tune parameter of a machine learning model) that overcome the cited abstract idea, and additionally may contain practical application and/or provide improvement the technology field.
Potentially Allowable Subject Matter
Claims 1-2, 7-9, 10-11 and 16-20 would be potentially allowable if amended to overcome the pertinent rejections under section of the double patenting rejections.
The following is a statement of reasons for the indication of potentially allowable subject matter:
With respect to Claim 1, The closest prior arts found during the search are as follows: Patel US 11631233, which discloses: “method 200 further comprises providing confidence score to enable analysis on quality of text extraction. Multiple confidence scores and an aggregate confidence scores are computed to assess quality of different extraction steps of the method. The confidence score computation includes: a) generating confidence scores for each of the classified document type, detected text boxes, the extracted text information and a name validator b) assigning predefined weights to each of the confidence scores c) aggregating the weighted confidence score to compute a cumulative confidence score for the extracted text information. Steps for confidence score calculation approach include multiple functional components for individual confidence score calculation. The components include a text detection component, a text extraction component, a string similarity component, a document classification component, and a name validator component a) Text Detection component:—It is a deep learning based model, which detects the text boundary boxes along with the text detection confidence score for every predicted text region in an input image. The confidence score is passed to the Confidence score calculator Component.” Which partially reads on receiving, by one or more processors, data corresponding to a data extraction program, the data being associated with at least a first file; executing, by the one or more processors, a confidence algorithm that causes the one or more processors to perform operations comprising: determining, based at least in part on the first confidence score from the entry in the dictionary of words, a content confidence score corresponding to the first field, and determining, based on the content confidence score and a position confidence score, a certainty score for the first field, the position confidence score being associated with a position of the first field within the first file; determining, by the one or more processors based at least in part on the certainty score, a certainty quotient for the first file indicating a reliability of the data corresponding to the data extraction program; and generating, by the one or more processors, a data object that indicates the certainty quotient. However, it is silent on generating a dictionary of words from one or more fields in the first file, wherein an entry in the dictionary of words corresponding to a first word in a first field of the first file includes a first confidence score associated with the first word, Also it does not explicitly disclose receiving step from a data extraction program file, lack the generating of dictionary of words for confidence scoring generation, does not clearly disclose position confidence score even though bounding box may imply spatial coordinates, although it mention aggregating confidence scores into cumulative score, to assess extraction quality, but it does not disclose “certainty quotient” for the combine score. See col. 13 and 14 for additional details.
Kasa US 20250322292 – discloses executing a confidence algorithm by one or more professors, and partially or implicitly discloses determining a certainty score for the first field based on the content confidence score and a position confidence score, “the base score technique 222 uses both positional average and field label average, and this information is also used by the pattern technique 224 to find the average confidence for related patterns based on an annotation threshold to satisfy the confidence score. In certain embodiments, the base score technique 222 uses the field label average percentage and uses text (extracted via Optical Character Recognition (OCR)) as an input and provides a confidence score indicating whether the text belongs to the correct field label. Then, the field label average score may be used by the semantic analysis technique 226 along with the sematic analysis score to determine whether the confidence score indicating whether the text belongs to the correct field label is correct” See para 0064. However, it silent generating the dictionary and confidence associated with the content, as well as other features of the claim as recited.
Singer US 9524518-teaches “With reference to FIG. 2, the product profile module 214 is a software component or application (e.g., a set of instructions residing in memory, such as memory 110 in FIG. 1) executable by a processing device (e.g., processing device 108 in FIG. 1) configured to receive the one or more information components and associated information (e.g., the information confidence score) from the parsing module 212. The product profile module 214 is configured to compare the information confidence score associated with an information component to a threshold. In an embodiment, if the information confidence score for a particular information component is greater than or equal to the threshold, the product profile module 214 adds the information component to the attribute data store 216.” See col. 6. Which reads on receiving, by one or more processors, data corresponding to a data extraction program, the data being associated with at least a first file; it further teaches: “one or more recognition models may be built for use with a well-defined dictionary in order to more accurately search for and identify words in the text segments extracted from the product image. For example, a product image relating to a food product may include a “Sodium 10 mg” text segment. In this example, a recognition model may be built which indicates a probability that a text segment of “Sodium” will appear in the left side of a column on a portion of the product's package and a particular probability may be assigned that the text segment “10 mg” will appear following the “Sodium” text segment. It is noted that in one embodiment, a separate text segment of “10”, “mg”, and “Sodium” may be identified and extracted and structured in accordance with the original product image. In an embodiment, the “Sodium” text segment and the “10 mg” text segment may be separated by some amount of whitespace. In this example, the OCR processing recognizes that the two text segments are related and combines them into a single information component including “Sodium 10 mg”, which partially reads on executing, by the one or more processors, a confidence algorithm that causes the one or more processors to perform operations comprising: generating a dictionary of words from one or more fields in the first file, wherein an entry in the dictionary of words corresponding to a first word in a first field of the first file includes a first confidence score associated with the first word, determining, based at least in part on the first confidence score from the entry in the dictionary of words, a content confidence score corresponding to the first field. Although it mentions recognition in dictionary context, it silent on generating dictionary of words from one or more fields in the first file. Additionally, although it mentions position/spatial probability, left side of column for example, however, it is silent on combining content confidence score to generate a certainty score for the field. It is further silent on the other features of the claim as recited. See col. 8 for additional information.
Accordingly, the prior art of record fails to explicitly teach or fairly suggest the invention set forth in claim 1. Other independent claims 10 and 19, although different in statutory category, but contains similar limitations as claim 1, therefore also contains potentially allowable subject matter. Further dependent claims 2, 7-9, 11, 16-18, and 20 inherit the potentially allowable subject matter from claims 1, 10, and 19 respectively, and thus, also contain potentially allowable subject matter by virtue of their dependency.
Allowable Subject Matter
Claims 3-6 and 12-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Notwithstanding, said aforementioned teachings of prior art cited is respectfully reconsidered and found to fail to teach or fairly suggest either individually or in a reasonable combination the presented limitations in claims 3-6 and 12-15, as specifically recited.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Golchha (US 20250045548) – discloses “An apparatus for generating a confidence score associated with a scanned label is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a profile, wherein the profile includes at least a label and a plurality of digital representations of slides, perform a flip detection process on the profile to verify the orientation of at least slide data of the profile, generate a scanned label as a function of the at least a label, determine a confidence score associated with the scanned label, adjust the confidence score for accuracy, and display the confidence score using a display device.” See Abstract for additional details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip H Lam whose telephone number is (571)272-1721. The examiner can normally be reached 9 AM-3 PM Pacific time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHILIP H LAM/ Examiner, Art Unit 2656
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