DETAILED ACTION
Claims 1-20 objected to because of the following informalities:
Claim(s) 1,5,6 and 8,12,13 and 15,19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelke et al. (US 2021/0274039 A1) in view of Jahjah et al. (US 2022/0043547 A1) and PENG et al. (CN 111582241 A) with SEARCH machine translation:
Claim(s) 2,4 and 9,11 and 16,18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelke et al. (US 2021/0274039 A1) in view of Jahjah et al. (US 2022/0043547 A1) and PENG et al. (CN 111582241 A) with SEARCH machine translation as applied in claims 1,5,6 and 8,12,13 and 15,19 above further in view of Lenzi et al. (Named Entity Recognition on Transcribed Broadcast News at EVALITA 2011):
Claim(s) 3 and 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelke et al. (US 2021/0274039 A1) in view of Jahjah et al. (US 2022/0043547 A1) and PENG et al. (CN 111582241 A) with SEARCH machine translation as applied in claims 1,5,6 and 8,12,13 and 15,19 above further in view of Zubac et al. (ROBUST EXTENDED TOKENIZATION FRAMEWORK FOR ROMANIAN BY SEMANTIC PARALLEL TEXTS PROCESSING).
Claim(s) 7 and 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelke et al. (US 2021/0274039 A1) in view of Jahjah et al. (US 2022/0043547 A1) and PENG et al. (CN 111582241 A) with SEARCH machine translation as applied in claims 1,5,6 and 8,12,13 and 15,19 above further in view of Burger et al. (US 2020/0265301 A1):
Response to Amendment
The amendment was received 2/2/2026. Claims 1-20 pending.
Claim Objections
Claims 1-20 objected to because of the following informalities:
Re claim 1, penultimate line’s
“the service entity recognition analysis” is objected for truncating claim 1, line 14’s
“the service entity recognition analysis result”.
Thus claims 2-7 are objected for depending on claim 1.
Claim 8 is rejected the same as claim 1.
Thus claims 9-14 are objected the same as claim 2-7.
Claim 15 is objected the same as claim 1.
Claim 15, line 2’s non-transitory computer-readable medium” is objected for missing the article “a” (illustrated below). Thus, “non-transitory computer-readable medium” is interpreted as –a non-transitory computer-readable medium--.
Thus claims 16-20 objected the same as claims 2-7.
Appropriate correction is required.
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Response to Arguments
II. Claim Objection
Applicant’s arguments, see remarks, page 8, filed2/2/2026, with respect to the claim objection of claims 16-20 have been fully considered and are persuasive. The claim objection of claim 16-2- has been withdrawn.
However due to the amendment, claims 1-20 are objected for truncating the claimed “the service entity recognition analysis result” as shown above.
III. Section 101 Rejections
Applicant’s arguments, see remarks, pages 8-13, filed 2/2/2026, with respect to 35 USC 101 have been fully considered and are persuasive. The 35 USC 101 rejection of claims 1-20 has been withdrawn.
IV. Art Rejections
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., Applicant’s remarks, pages 13,14: an accurately identified improperly bounded entity:
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) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant's arguments filed 2/2/2026 have been fully considered but they are not persuasive. Applicants state in pages 13,14:
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The examiner respectfully disagrees since Engelke (US 2021/0274039 A1) teaches under the broadest reasonable interpretation of claim 1, shown as non-strike-thru text:
generating software instructions tailored (“customize voice-to-text software” [0196] last S: software customized/tailored: fig. 2:60: “Pre-Trained CA Voice-to-Text Software”) to the service (“a captioning service” [0021] 2nd S: fig. 1) associated with the service entity recognition analysis (“a context analysis of each word” [0207] 2nd S) 1.
35 USC § 101- Positive Statement
The claims are statutory in view of applicant’s remarks of 2/2/2026 regarding 35 USC 101.
In addition, claim 15’s “A computer program product…the computer program product comprising2 34 non-statutory5 embodiments (e.g., “a computer program product comprising software instructions” applicant’s disclosure [0061], 2nd S) in view of applicant’s disclosure [0011][0061]:
“a computer program product includes at least one non-transitory computer-readable storage medium storing software instructions” [0011], 1st S and
“a computer program product comprising software instructions stored on at least one non-transitory computer-readable storage medium (e.g., memory 204).” [0061], 2nd S:
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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 identically67 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.
Claim(s) 1,5,6 and 8,12,13 and 15,19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelke et al. (US 2021/0274039 A1) in view of Jahjah et al. (US 2022/0043547 A1) and PENG et al. (CN 111582241 A) with SEARCH machine translation:
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Re 1., Engelke teaches A machine learning8 (closest mapping to “artificial intelligence to recognize …a word or phrase9” [0564] 1st S; thus, Engelke does not teach “machine learning”) method (“a method” [0130] 1st S: figures 2 & 3) for determining accuracy of text (“error” [0924]) recognition (“in the displayed text” [0176] 7th S: fig. 20:654,660: recognition of the “jobs” error corrected to “joining”), the machine learning method (likewise) comprising10:
receiving (this comma phrase is not limiting under the broadest reasonable interpretation and thus is struck-out in view of the non-exhaustive list in MPEP 2143.03, 4th para)11 comprising two (this Markush alternative “more” is not limiting under the broadest reasonable interpretation and thus is struck-out in view of a list of alternatives in MPEP 2143.03, 3rd para) analyses12 of a text (or likewise “the processor13 adaptively trains the voice model using…analysis of each text14” [0207] 1st two Ss: fig. 15:512: “Train Voice Model using Transcription Errors”:
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);
generating by classification circuitry (or likewise “ “labeled”-“caption device” [0324] 2nd S) of the computer and using the first data structure,15 a second data structure comprising16 superstrings1718 (figs. 17,18,24: strings19 on a display) common20 (or likewise “Sharing Text with HU” [0256]: fig. 24) to21 the two
wherein the second data structure defines (via likewise outlined-text-boxes in fig. 24:804)
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);
identifying, 22232425 an accurately identified (entity: i.e., “accurately transcribed”26 “word”) entity 2728 between the two or more entity spans as an error (or likewise “errors”, [0027] 1st S, from other text);
classifying29 the error into at least one category of a group of categories (or likewise “categorize each error” [0641] 1st S); c
generating, by (“Truth/” [0638]) scoring circuitry (or likewise “scoring”-systems” [0600] 1st S) of the computer and based on the (classified) error that is classified into the at least one category, a third data structure30 indicating a score report31 (or “scorer” “fields”32 [0643] last S comprising report “inputs” (or report outputs) [0232]): as shown in fig. 1) for a (“communication” [0006]) service (or “transcription services” [0137] last S) associated with a (“captioning” [0041] via fig. 8) service33 (or relay server resulting in “compared text” “Characteristics analyzed” [0503] reproduced below) entity34 (“word” “Characteristics analyzed” [0503] 3rdS) recognition35 (“identical” “Characteristics analyzed” [0503] 3rd S) analysis result36 (or said “Characteristics analyzed”) of the text; and
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generating instructions37 tailored38(or likewise “customize voice-to-text software” [0196] last S) to39 the service associated with the service entity recognition analysis 40.
Engelke does not teach under the non-broadest reasonable interpretation the difference of claim 1 of:
A. (A) machine learning (method)41…(the) machine learning (method)…42
B. improperly (bounded).
Engelke does not teach under the broadest reasonable interpretation the difference of claim 1 of:
A. (A) machine learning (method)…(the) machine learning (method).
Jajjah teaches under the non-broadest reasonable interpretation the difference (B) of claim 1 of:
B. improperly (bounded) via “incorrectly…bounding” [0018]: fig. 6:610: box too small. Since Engelke teaches a box (fig. 52: 1656: “catch”), one of skill in boxes can make Enhelke’s be as Jahjah’s predictably recognizing the change providing “a correction to an incorrect… bounding box”4344, Jahjah [0019]:
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Engelke of the combination of Engelke,Jahjah does not teach under the non-broadest reasonable interpretation/broadest reasonable interpretation the remaining difference (A) of claim 1:
A. (A) machine learning (method)45…(the) machine learning (method)…46
PENG teach under the non-broadest reasonable interpretation/broadest reasonable interpretation the remaining difference (A) of claim 1:
A. (A) machine learning (“machine learning”, pg. 14, last txt blk) (method)47…(the) machine learning (method)…48
Since Engelke of the combination of Engelke,Jahjah teaches artificial intelligence, one of ordinary skill in the art of artificial intelligence can make Engelke’s of the combination of Engelke,Jahjah be as PENG’s machine learing and “artificial intelligence” (PENG, pg. 14 last txt blk) seeing in the change the artificial intelligence “ to continuously improve the performance of itself”, PENG, pg. 14, last txt blk, thus improving artificial intelligence text/word/phrase recognition.
Re 5., Engelke of the combination of Engelke,Jahjah,PENG teaches The machine learning method of claim 1, further comprising:
selecting, by the classification circuitry,49 the at least one category of the group of categories consisting50 of a correct category, an incorrect category (or “categories referred to herein as…errors”5152 [0450] 2nd S), a missing category, a spurious category, or a partial category.
Re 6. Engelke of the combination of Engelke,Jahjah,PENG teaches The machine learning method of claim 1, further comprising:
retrieving, by a communication circuitry,53 a copy (“archived”54 [0632] 2nd S) of the score (field) report from memory (understood given field); and
sending (or transmitting), by the communication circuitry,55 the (archived) copy of the score (field) report to another (“display screen” [0882] 4th S) entity.
Claim 8 is rejected like claim 1:
Re 8., Engelke of the combination of Engelke,Jahjah,PENG teaches An apparatus for determining accuracy of text recognition using machine learning, the apparatus comprising:
communications circuitry configured to receive a first data structure comprising two or more analyses of a text;
classification circuitry configured to:
generate, using the first data structure,a second data structure comprising superstrings common to the two or more analyses of the text, wherein the second data structure defines
identify an accurately identified entity and an improperly bounded entity between the two or more entity spans as an error, and
classify the error into at least one category of a group of categories; and
scoring circuitry configured to:
generate, based on the error that is classified into the at least one category, a third data structure indicating a score report for a service associated with a service entity recognition analysis result of the text, and
generate software instructions tailored to the service associated with the service entity recognition analysis to correctly identify the improperly bounded entity.
Claim 12 is rejected like claim 5:
Re 12., Engelke of the combination of Engelke,Jahjah,PENG teaches The apparatus of claim 8, wherein the classification circuitry is further configured to select the at least one category of the group of categories consisting of a correct category, an incorrect category, a missing category, a spurious category, or a partial category.
Claim 13 is rejected like claim 6:
Re 13., Engelke of the combination of Engelke,Jahjah,PENG teaches The apparatus of claim 8, further comprising:
communication circuitry configured to:
retrieve a copy of the score report from memory, and
send the copy of the score report to another entity.
Claim 15 is rejected like claims 1 and 8:
Re 15. (Currently Amended), Engelke of the combination of Engelke,Jahjah, PENG teaches A computer program product for determining accuracy of text recognition using machine learning, the computer program product56 comprising57 non-transitory computer-readable storage medium58 storing software instructions that, when executed, cause an apparatus to:
receive a first data structure comprising two or more analyses of a text;
generate, using the first data structure, a second data structure comprising common to the two or more analyses of the text, wherein the second data structure defines
identify an accurately identified entity and an improperly bounded entity between the two or more entity spans as an error;
classify the error into at least one category of a group of categories;
generate, based on the error that is classified into the at least one category, a third data structure indicating a score report for a service associated with a service entity recognition analysis result of the text; and
generate second software instructions tailored to the service associated with the service entity recognition analysis to correctly identify the improperly bounded entity.
Claim 19 is rejected like claims 5 and 12:
Re 19., Engelke of the combination of Engelke,Jahjah,PENG teaches The computer program product of claim 15, wherein the software instructions that, when executed, further cause the apparatus to:
select the at least one category of the group of categories consisting of a correct category, an incorrect category, a missing category, a spurious category, or a partial category.
Claim(s) 2,4 and 9,11 and 16,18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelke et al. (US 2021/0274039 A1) in view of Jahjah et al. (US 2022/0043547 A1) and PENG et al. (CN 111582241 A) with SEARCH machine translation as applied in claims 1,5,6 and 8,12,13 and 15,19 above further in view of Lenzi et al. (Named Entity Recognition on Transcribed Broadcast News at EVALITA 2011):
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Re 2., Engelke of the combination of Engelke,Jahjah,PENG teaches The machine learning method of claim 1, further comprising:
receiving, by communication circuitry, input text data (“to provide text” [0314] 2nd to last S) comprising the text (fig. 17);
generating, by segmentation circuitry and based on the (provided) input text data, service analyzed (via said resulting in “analyzed”, 3rd S, identical59 service captioning entity60 characteristics61 capable of being recognized) tokenized text (“accuracy” [0195] last S) data, wherein the service analyzed tokenized text (accuracy) data is generated using a text analyzation (via “text word” “analysis” [0207] 2nd S) service (via “analysis” “server” [0445] penult S); and
generating, by the segmentation circuitry and based on the input text (accuracy) data, gold tokenized text (analysis) data, wherein the gold tokenized text (analysis) data is generated using a gold (characteristic) entity analyzation (via “text word” “analysis” [0207] 2nd S) service (server).
Engelke of the combination of Engelke,Jahjah,PENG does not teach “
tokenized…
tokenized…
gold tokenized…
gold tokenized…
gold”.
Lenzi teaches:
tokenized (resulting in “the gold standard token, the gold standard Entity tag” (pg. 91, 4th para, 2nd S) …
tokenized…
gold tokenized (resulting in “the gold standard token, the gold standard Entity tag” (pg. 91, 4th para, 2nd S)…
gold tokenized…
gold.
Since Engelke of the combination of Engelke,Jahjah,PENG teaches transcription, one of skill in transcriptions can make Engelke’s (manual transcription) of the combination of Engelke,Jahjah,PENG be as Lenzi’s predictably recognizing the change resulting in higher recognition of people names (PER), political names (GPE), location names (LOC), organization names (ORG) as indicated in Lenzi’s Table 4: precision column (“Prec.”) of automated transcription verses Lenzi’s Table 5 of manual gold transcription.
Re 4., Engelke of the combination of Engelke,Jahjah,PENG teaches The machine learning method of claim 1, further comprising:
determining, by the classification circuitry, a difference between (“those captions and the ASR captions to the AU captioned device for a second round of error corrections” [0394]) a service62 analyzed (via said relay server characteristics analyzed) tokenized text (on a display: e.g., fig. 17) and a gold tokenized text (on a display: e.g., fig. 18);
determining, by the classification circuitry,63 an accuracy of (“error” [0924]) entity recognition in the service entity recognition analysis result (or said “Characteristics analyzed”) of the text corresponding to the two or more entity spans based on the difference between (“those captions and the ASR captions to the AU captioned device for a second round of error corrections” [0394]) the service analyzed (via said relay server characteristics analyzed) tokenized text and the gold tokenized text; and
classifying, by the classification circuitry,64 each of the two or more entity spans (“as visible…invisible…or minor” [0641] 1st S) based on the accuracy of the (“error” [0924]) entity recognition in the service entity recognition analysis result (or said “Characteristics analyzed”) of the text corresponding to the two or more entity spans.
Engelke of the combination of Engelke,Jahjah,PENG does not teach the difference of (similar to rejection of claim 2):
Tokenized…
Gold tokenized…
Tokenized…
Gold tokenized…
Since Engelke of the combination of Engelke,Jahjah,PENG teaches transcription, one of skill in transcriptions can make Engelke’s (manual transcription) of the combination of Engelke,Jahjah,PENG be as Lenzi’s predictably recognizing the change resulting in higher recognition of people names (PER), political names (GPE), location names (LOC), organization names (ORG) as indicated in Lenzi’s Table 4: precision column (“Prec.”) of automated transcription verses Lenzi’s Table 5 of manual gold transcription.
Claim 9 is rejected like claim 2:
Re 9., Engelke of the combination of Engelke,Jahjah,PENG,Lenzi teaches The apparatus of claim 8, further comprising:
communication circuitry configured to receive input text data comprising the text; and
segmentation circuitry configured to:
generate, based on the input text data, service analyzed tokenized text data, wherein the service analyzed tokenized text data is generated using a text analyzation service, and
generate, based on the input text data, gold tokenized text data, wherein the gold tokenized text data is generated using a gold entity analyzation service.
Claim 11 is rejected like claim 4:
Re 11. , Engelke of the combination of Engelke,Jahjah,PENG,Lenzi teaches The apparatus of claim 8, wherein the classification circuitry is further configured to:
determine a difference between a service analyzed tokenized text and a gold tokenized text;
determine an accuracy of entity recognition in the service entity recognition analysis result of the text corresponding to the two or more entity spans based on the difference between the service analyzed tokenized text and the gold tokenized text; and
classify each of the two or more entity spans based on the accuracy of the entity recognition in the service entity recognition analysis result of the text corresponding to the two or more entity spans.
Claim 16 is rejected like claims 2 and 9:
Re 16., Engelke of the combination of Engelke,Jahjah,PENG,Lenzi teaches The computer program product of claim 15, wherein the software instructions that, when executed, further cause the apparatus to:
receive input text data comprising the text;
generate, based on the input text data, service analyzed tokenized text data, wherein the service analyzed tokenized text data is generated using a text analyzation service; and
generate, based on the input text data, gold tokenized text data, wherein the gold tokenized text data is generated using a gold entity analyzation service.
Claim 18 is rejected like claims 4 and 11:
Re 18., Engelke of the combination of Engelke,Jahjah,PENG,Lenzi teaches The computer program product of claim 15, wherein the software instructions that, when executed, further cause the apparatus to:
determine a difference between a service analyzed tokenized text and a gold tokenized text;
determine an accuracy of entity recognition in the service entity recognition analysis result of the text corresponding to the two or more entity spans based on the difference between the service analyzed tokenized text and the gold tokenized text; and
classify each of the two or more entity spans based on the accuracy of the entity recognition in the service entity recognition analysis result of the text corresponding to the two or more entity spans.
Claim(s) 3 and 10 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelke et al. (US 2021/0274039 A1) in view of Jahjah et al. (US 2022/0043547 A1) and PENG et al. (CN 111582241 A) with SEARCH machine translation as applied in claims 1,5,6 and 8,12,13 and 15,19 above further in view of Zubac et al. (ROBUST EXTENDED TOKENIZATION FRAMEWORK FOR ROMANIAN BY SEMANTIC PARALLEL TEXTS PROCESSING).
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Re 3., Engelke of the combination of Engelke,Jahjah,PENG teaches The machine learning method of claim 1, further comprising:
segmenting, by a segmentation circuitry, a service65 analyzed66 tokenized67 text68 data (“to perform in line corrections to the text” [0726] penult S) and a gold tokenized text data into the
Engelke of the combination of Engelke,Jahjah,PENG does not teach:
“service analyzed tokenized…
gold tokenized”.
Zubac teaches:
a (“semantic”, pg. 31, 4. THE TOKENICATION CLASS DISAMBIGUATION MODULE, 1st S) service analyzed (text via “Google Translate and Microsoft Bing”, pg. 32, 1st para, 6th S providing a semantic69 analysis70: determining which word sense grammatically fits with other grammatical parts of a sentence) tokenized (“units”, pg.18, 2.1 What is a token? What is tokenization?, 3rd S, reproduced below)…
gold tokenized (via the tolkenizer of equation (1) resulting in “the Gold standard…token”, pg. 25. 2.3.5. Tolkenizers evaluation, sentence before gold tokenizer equation (1))…
Since Engelke of the combination of Engelke,Jahjah,PENG teaches an “automated text” “segment” [0151] penult S, one of skill in the art of text segments can make Engelke’s of the combination of Engelke,Jahjah,PENG be as Zubac’s predictably recognizing the change providing superior segmentation of text via “quality tokenization for next text processing steps”, Zubac, 6. CONCLUSION AND FUTURE WORKS, 2nd S, wherein tokenization “refers to segmentation of a written text”, Zubac, , pg.18, 2.1 What is a token? What is tokenization?, reproduced below:
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Claim 10 is rejected like claim 3
Re 10., Engelke of the combination of Engelke,Jahjah,PENG,Zubac teaches The apparatus of claim 8, further comprising:
segmentation circuitry configured to segment a service analyzed tokenized text data and a gold tokenized text data into the
Claim 17 is rejected like claim 10:
Re 17., Engelke of the combination of Engelke,Jahjah,PENG,Zubac teaches The computer program product of claim 15, wherein the software instructions that, when executed, further cause the apparatus to:
segment a service analyzed tokenized text data and a gold tokenized text data into the .
Claim(s) 7 and 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Engelke et al. (US 2021/0274039 A1) in view of Jahjah et al. (US 2022/0043547 A1) and PENG et al. (CN 111582241 A) with SEARCH machine translation as applied in claims 1,5,6 and 8,12,13 and 15,19 above further in view of Burger et al. (US 2020/0265301 A1):
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Re 7., Engelke of the combination of Engelke,Jahjah,PENG teaches The machine learning method of claim 1, further comprising:
Selecting (fig. 15:504: “AU Select Help?”: 518: “AU Select Caption?”), by the classification circuitry,71 supplementary training data(“base” [1113]) based on one or more of (A) an error number (fig. 15:512: “Train Voice Model Using Transcription Errors”) and (B) an error type; and
updating, by the classification circuitry,72 an operation (resulting in an “updated” “interface”73 [0571] last S) of a text74 analysis (or “text” “Characteristics analyzed” [0503], 3rd S) service (via said comparing server-processor analyzed [0503]).
Engelke of the combination of Engelke,Jahjah,PENG does not teach “supplementary” (training data). Burger teaches “supplementary” (training data via (“additional training data”75 [0106]). Since Engelke of the combination of Engelke, Jahjah,PENG teaches a training database, one of skill in the art of data cam make Engelke’s of the combination of Engelke,Jahjah,PENG be as Burger’s predictably recognizing the change being “helpful to supplement the training set and to make the neural network model more accurate” , Burger [0092] last S, in classification of errors.
Claim 14 is rejected like claim 7:
Re 14., Engelke of the combination of Engelke,Jahjah,PENG,Burger teaches The apparatus of claim 8, further comprising:
selecting, by the classification circuitry, supplementary training data based on one or more of an error number and an error type; and
updating, by the classification circuitry, an operation of a text analysis service.
Claim 20 is rejected likes claims 13,14:
Re 20., Engelke of the combination of Engelke,Jahjah,PENG,Burger teaches The computer program product of claim 15, wherein the software instructions that, when executed, further cause the apparatus to:
retrieve a copy of the score report from memory;
send the copy of the score report to another entity;
select supplementary training data based on one or more of an error number and an error type; and
update an operation of a text analysis service.
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
BAO et al. (CN 107943860 A) with SEARCH machine translation
BAO teaches “not” accurate “machine learning” via page 7, 5th txt blk:
“compared to the traditional machine learning for text recognition accuracy is not high”
as the closest to the claimed “machine learning method for determining accuracy” of claim 1.
Wang et al. (US 8,903,136 B1)
Wang teaches an accurate “machine learning algorithm” “OCR application” via c.12,ll.45-50:
“For example, the OCR76 application 115 may use a machine learning algorithm to determine the likelihood that a digit is correct77.”
as the closest to the claimed “machine learning method for determining accuracy” of claim 1.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM EST.
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/DENNIS ROSARIO/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
1 This struck-out phrase “to correctly identify the improperly bounded entity” has been rendered “optional” and thus is outside “the broadest reasonable claim interpretation” of claim 1 in view of MPEP 2143.03, 2nd para--As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language (“an accurately identified entity and an improperly bounded entity”) that suggests (“and” suggests Markush alternatives) or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009)--.
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2 MPEP 2111.03 I. COMPRISING, 1st para, 1st S: The transitional term "comprising", which is synonymous with "including," "containing," or "characterized by," is inclusive or open-ended and does not exclude additional, unrecited elements (“software instructions” [0061] 2nd S) or method steps, wherein “by” is a prepositional modifier modifying the recited (claimed) element “computer program product” as—a non-transitory computer-readable storage medium computer program product—or in an alternative as, for example: a non-transitory computer-readable storage product or a non-transitory computer-readable medium product.
3 MPEP 2111.03 Transitional Phrases [R-01.2024] The transitional phrases "comprising", "consisting essentially of" and "consisting of" define the scope of a claim with respect to what unrecited additional components or steps, if any, are excluded from the scope of the claim. The determination of what is or is not excluded by a transitional phrase must be made on a case-by-case basis in light of the facts of each case.
4 Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d, pages 29,30, footnote 10: “exclusive…point…excluding the carrier waves embodiment”.
5MPEP 2106.03 II ELIGIBILITY STEP 1: WHETHER A CLAIM IS TO A STATUTORY CATEGORY, 4th para:-- For example, the BRI of machine readable media can encompass non-statutory transitory forms of signal transmission, such as a propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the BRI encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a "machine-readable medium" were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves).—wherein scope is defined: Linguistics, Logic. the range of words or elements of an expression over which a modifier (a patent examiner) or operator (or me) has control. (Dictionary.com)
6 identical: similar or alike in every way, wherein similar is defined: having a likeness or resemblance, especially in a general way. (Dictionary.com)
7 identical: being the same : selfsame, wherein same is defined: resembling in every relevant respect, wherein resemble is defined: to be like or similar to, wherein similar is defined: having characteristics in common : strictly comparable (MerriamWebster.com)
8 machine learning: a branch of artificial intelligence in which a computer generates rules underlying or based on raw data that has been fed into it (Dictionary.com)
9 artificial intelligence: the branch of computer science involved with the design of computers, robots, programmed devices, and software applications having the capacity to imitate human intelligence and thought. AI, A.I, wherein computer science is defined: the science that deals with the theory and methods of processing information in digital computers, the design of computer hardware and software, and the applications of computers.(Dictionary.com)
10 BROAD CLAIM LANGUGAGE: -ing (of “comprising” or any -ing word in the claim set): a suffix of nouns formed from verbs, expressing the action of the verb or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too. (Dictionary.com)
11 data structure: an organized form, such as an array list or string, in which connected data items are held in a computer, wherein form is defined: due or proper shape; orderly arrangement of parts; good order. (Dictionary.com)
12 analyses: the plural of analysis., wherein analysis is defined
13 processor: A part of a computer, such as the central processing unit, that performs calculations or other manipulations of data, wherein data is defined: (usually used with a singular verb) information in digital format, as encoded text or numbers, or multimedia images, audio, or video, wherein format is defined: Computers. the arrangement of data for computer input or output, such as the number and size of fields in a record or the spacing and punctuation of information in a report, wherein arrangement is defined: an act of arranging; state of being arranged, wherein arrange is defined: to place in proper, desired, or convenient order; adjust properly, wherein order is defined: formal disposition or array, wherein array is defined: a large and impressive grouping or organization of things, wherein field is defined: Computers.
one or more related characters treated as a unit and constituting part of a record, for purposes of input, processing, output, or storage by a computer.(Dictionary.com)
14 text: the actual wording of anything written or printed. (Dictionary.com)
15 This “by” phrase is outside of the broadest reasonable interpretation (BRI)
16 comprising: to include or contain, wherein contain is defined: to be equal to. (Dictionary.com)
17 super-: a prefix occurring originally in loanwords from Latin, with the basic meaning “above, beyond.” Words formed with super- have the following general senses: “to place or be placed above or over” (superimpose; supersede ),…, wherein over is defined: on or on top of (a display) (Dictionary.com)
18 string: any series of things arranged or connected in a line or following closely one after another: (Dictionary.com)
19 string: Computers, Linguistics. a linear sequence of symbols, words, characters, or bits that is treated as a unit, wherein bit is defined: Also called binary digit. a single, basic unit of digital information that is represented by one of two values, such as 1 or 0, True or False, or Yes or No. (Dictionary.com)
20 common: belonging equally to, or shared alike by, two or more or all in question. (Dictionary.com)
21 to: (used for expressing agreement or accordance [fig. 24: “Current AVR Accuracy”]) according to; by. (Dictionary.com)
22 comma: the punctuation mark(,) indicating a slight pause in the spoken sentence and used where there is a listing of items or to separate a nonrestrictive clause or phrase ( “by the classification circuitry of the computer”) from a main clause (claim 1) (Dictionary.com)
23 comma: the sign (,), a mark of punctuation used for indicating a division in a sentence, as in setting off a word, phrase, or clause, especially when such a division is accompanied by a slight pause or is to be noted in order to give order to the sequential elements of the sentence. (Dictionary.com)
24 Outside BRI
25 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 4th para:
--The following types of claim language may raise a question as to its limiting effect (this list is not exhaustive:
I am adding to the list, that may raise a question as to a claim language’s limiting effect, non-restrictive or Non-:Limiting comma Phrases: NLPs:--for example the claimed :-- ,by the classification circuitry of the computer,-- is an NLP and also does not give order to the sequential elements of claim 1)
• preamble (MPEP § 2111.02);
• clauses such as "adapted to," adapted for," "wherein," and "whereby" (MPEP § 2111.04, subsection I);
• contingent limitations (MPEP § 2111.04, subsection II);
• printed matter (MPEP § 2111.05); and
• functional language associated with a claim term (MPEP § 2181).
26 transcribed: translate or transliterate, wherein translate is defined: interpret, wherein interpret is defined: to construe or understand in a particular way, wherein understand is defined: to perceive the meaning of, wherein perceive is defined: to recognize, discern, envision, or understand (Dictionary.com)
27 and: (used to connect alternatives). (Dictionary.com)
28 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, 2nd S: Language (“and”) that suggests (“and” suggests connecting Markush alternatives: “an accurately identified entity and an improperly bounded entity”) or makes a feature (“an accurately identified entity and an improperly bounded entity”) or step (claim 1, last line: “to correctly identify the improperly bounded entity”) optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives (“an accurately identified entity and an improperly bounded entity”), the prior art teaches the element if one of the alternatives is taught by the prior art.
29 Outside BRI
30 data structure: an organized form, such as an array list or string, in which connected data items are held in a computer, wherein form is defined: due or proper shape; orderly arrangement of parts; good order.
31 report: an account or statement describing in detail an event, situation, or the like, usually as the result of observation, inquiry, etc. (Dictionary.com)
32 field: Computers. one or more related characters treated as a unit and constituting part of a record, for purposes of input, processing, output, or storage by a computer, wherein input is defined: Computers., to enter (data) into a computer for processing, wherein data is defined: information, wherein information is defined: Computers. important or useful facts obtained as output from a computer by means of processing input data with a program, wherein output is defined: Computers. information in a form suitable for transmission from internal to external units of a computer, or to an outside medium, wherein transmission is defined: the act or process of transmitting, wherein transmitting is defined: to communicate, as information or news, wherein as is defined: in the role, function, or status of, wherein news is defined: a report of a recent event , wherein data is previously defined highlighting data- structure features: “arrangement” “order” “organization” (Dictionary.com)
33 Coordinate adjective (i.e., recognition & entity & service analysis result)
34 Coordinate adjective (i.e., entity & recognition & service analysis result)
35 Coordinate adjective (i.e., entity & service & recognition analysis result)
36 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, 2nd S: Language (“a service entity recognition analysis result”) that suggests or makes a feature or step optional (“service entity recognition analysis” suggests alternative coordinate-adjectives: -service & entity & recognition & analysis) but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives (“service entity recognition analysis”) , the prior art teaches the element if one of the alternatives is taught by the prior art.
37 “instructions” is the object of “tailored”
38 “tailored” is a past participle, wherein past participle is defined: a participial form of verbs (tailor) used to modify a noun (“instructions”) that is logically the object of a verb (tailor), also used in certain compound tenses and passive forms of the verb in English and other languages (Dictionary.com)
39 to: (used for expressing destination or appointed end). (Dictionary.com)
40 As discussed previously, the claimed “to correctly identify the improperly bounded entity” is struck-out in view of MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para, 2nd S: Language (“and”) that suggests (“and” suggests connecting Markush alternatives) or makes a feature (“an accurately identified entity and an improperly bounded entity”) or step (claim 1, last line: “to correctly identify the improperly bounded entity”) optional but does not require that feature or step (claim 1, last line: “to correctly identify the improperly bounded entity”) does not (prepositionally) limit the scope of a claim under the broadest reasonable claim interpretation
41 (italics) represent claim limitations already taught
42 ellipses (…) represent claim limitations already taught
43 incorrect: improper, unbecoming, or inappropriate (Dictionary.com)
44 “incorrect” is a cumulative adjective modifying “bounding” which in turn modifies “box”
45 (italics) represent claim limitations already taught
46 ellipses (…) represent claim limitations already taught
47 (italics) represent claim limitations already taught
48 ellipses (…) represent claim limitations already taught
49 Outside BRI
50 Close-ended?
51 refer to: mean or denote. (Dictionary.com)
52 error: the condition of believing what is not true, wherein true is defined: correct (Dictionary.com)
53 Outside BRO
54 archive: Digital Technology., to compress (computer files) and store them in a single file, wherein file is defined: A collection of related data or program records stored as a unit with a single name. Files are the basic units that a computer works with in storing and retrieving data, wherein data is defined: information, wherein formation is defined: Computers. A) important or useful facts obtained as output from a computer by means of processing input data with a program. B) data at any stage of processing (input, output, storage, transmission, etc.).
55 Outside BRI
56 [0011]In a further example embodiment, a computer program product is provided for automatically determining accuracy of entity recognition of text, the computer program product includes at least one non-transitory computer-readable storage medium storing software instructions that, when executed, cause an apparatus to segment a service entity recognition analysis of the text and a gold entity recognition analysis of the text into common superstrings that define entity spans. The software instructions, when executed, also cause the apparatus to classify each of the entity spans based on an accuracy of entity recognition in the service entity recognition analysis of the text corresponding to the entity spans using a classification system that differentiates accurately identified but improperly bounded entities into at least three subcategories to obtain an entity accuracy classification. The software instructions, when executed, further cause the apparatus to obtain, for a service that generated the service entity recognition analysis of the text, a score report based on the entity accuracy classification. The software instructions, when executed, additionally cause the apparatus to perform an action set based on the entity accuracy classification.
[0061]As will be appreciated based on this disclosure, example embodiments contemplated herein may be implemented by an apparatus 200. Furthermore, some example embodiments may take the form of a computer program product comprising software instructions stored on at least one non-transitory computer-readable storage medium (e.g., memory 204). Any suitable non-transitory computer-readable storage medium may be utilized in such embodiments, some examples of which are non-transitory hard disks, CD-ROMs, flash memory, optical storage devices, and magnetic storage devices. It should be appreciated, with respect to certain devices embodied by apparatus 200 as described in FIG. 2, that loading the software instructions onto a computing device or apparatus produces a special-purpose machine comprising the means for implementing various functions described herein.
57 comprise: to include or contain, wherein contain is defined: to be equal to. (Dictionary.com) .
58 Regarding 35 USC 101: The claimed “the computer program product comprising non-transitory computer-readable storage medium” has antecedent basis to/conforms to applicant’s [0011]’s 1st S: “the computer program product includes at least one non-transitory computer-readable storage medium” and thus is interpreted via MPEP 2111 (37 CFR 1.75(d)(1)) in view of said [0011]’s 1st S and thus not interpreted in view of applicant’s [0061] ‘s 2nd S: “computer program product comprising software instructions” being non-conforming via MPEP 2111 (37 CFR 1.75(d)(1)), wherein conform is defined: to be or become similar in form, nature, or character (usually followed by to ) (Dictionary.com).
59 identical: similar or alike in every way, wherein similar is defined: having a likeness or resemblance, especially in a general way, wherein likeness is defined: the state or fact of being like, wherein like is defined: corresponding or agreeing in general or in some noticeable respect, wherein noticeable is defined: capable of being noticed, wherein noticed is defined: to perceive, wherein perceive is defined: to recognize, discern, envision, or understand (Dictionary.com)
60 entity: thing (Dictionary.com) (broad term)
61 characteristic: Also characteristical. pertaining to, constituting, or indicating the character or peculiar quality of a person or thing
62 cumulative adjective
63 Outside BRI
64 Outside BRI
65 Cumulative adjective
66 Coordinate adjective
67 Coordinate adjective
68 Cumulative adject
69 semantic: of or relating to meaning or arising from distinctions between the meanings of different words or symbols (Dictionary.com)
70 analysis: linguistics the use of word order together with word function to express syntactic relations in a language, as opposed to the use of inflections Compare synthesis (Dictionary.com)
71 Outside BRI
72 Outside BRI
73 Interface: The layout of an application's graphic or textual controls in conjunction with the way the application responds to user activity. (Dictionary.com)
74 Cumulative adjective
75 additional: added; more; supplementary. (Dictionary.com)
76 OCR: optical character recognition. (Dictionary.com)
77 correct: to set or make true, accurate, or right; remove the errors or faults from (Dictionary.com)
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