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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/16/2026 has been entered.
Claims 1 and 11 have been amended, claims 3-10 and 13-20 have been canceled, and no claims have been added.
In light of Applicant’s amendment, previous claim rejections based on 35 USC 112(b), with respect to claims 1-10, have been withdrawn.
Claims 1-2 and 11-12 are pending with claims 1 and 11 as independent claims.
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 and 11-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more. Claim 1 (and similarly claim 11) recites “
identifying, by the computer program, a subject for the document, wherein the identification includes a classification of the document;
identifying, by the computer program, trigger words in the text of the document based on the plurality of triggers from the trigger word database and
applying a first graphical pattern to the identified trigger words;
limiting, by the computer program, a number of identified trigger words in a paragraph of the text to a maximum value and, when more than the maximum value are identified,
selectively de-identifies certain words as trigger words based on proximity constraints including no more than three consecutive trigger words and no more than three trigger words out of five words, wherein the maximum value is set based on machine learning;
identifying, by the computer program, skim-through words in the text of the document based on the plurality of skim-through words from the skim-through words database and
applying a second graphical pattern to the identified skim-through words, wherein the sim-through words include prepositions;
determining, by the computer program, that a percentage of the identified trigger words in at least a portion of the document is below a threshold.
Wherein the determining comprises comparing a number of the identified trigger words in the portion to a total number of words in the portion. The functions in underlined, bold can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper because identifying and classifying content fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection Ill, therefore the claim is reciting a mental process.
This judicial exception is not integrated into a practical application because the mental process is merely applied using a general-purpose computer (a computer program executed on a user electronic device and causing, by the computer program, the document to be output with the identified trigger words output in the first graphical pattern and the skim-through words being output in the second graphical pattern and the machine learning), with the operations comprising insignificant extra-solution activity.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of
receiving, by a computer program executed on a user electronic device a document comprising text, wherein the user electronic device comprises a display;
retrieving, by the computer program, a plurality of trigger words from a trigger word database specific to the subject and a plurality of skim-through words from a skim-through words database specific to the subject, are insignificant extra-solution activity that includes pre-solution activity of mere data gathering.
The additional elements of in response to the determination, outputting, by the computer program to the display, no changes for the portion of the document and outputting, by the computer program to the display, the document with the identified trigger words output in the first graphical pattern, and the skim-through words being output in the second graphical pattern and based on the threshold,
wherein words in the text that are not identified trigger words and are not identified skim-through words are output in a third graphical pattern, wherein the third graphical pattern is a neutral graphical pattern,
wherein the second graphical pattern visually brings the identified skim-through words to a background of the document and comprises a low-contrast color and a decreased font size,
wherein the third graphical pattern is selected responsive to backgrounds of the document and graphical properties of core words based on the subject, are insignificant extra-solution activity that includes post-solution activity that would not integrate the judicial exception into practical application.
The limitations of “receiving a document comprising text”, “outputting to display the document with trigger words, skim-through words, and words neither trigger wors nor skim-through words” are considered as "extra-solution activity" that includes pre-solution and post-solution activities, respectively, as described in MPEP § 2106.0S(g)- Insignificant Extra-Solution Activity [R-10.2019], " ... The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent ... " the step of “receiving” is pre-solution activity and the step “outputting” is post-solution activity.
This judicial exception is, also, not integrated into a practical application because the mental process is merely applied using a general-purpose computer (a trigger word database comprising a plurality of trigger words; a skim-through database comprising a plurality of skim-through words; an electronic device comprising: a display; a computer processor; and a memory storing a document readability computer program comprising instructions that, when executed by the computer processor), as recited in claim 11, with the operations comprising insignificant extra-solution activity. The additional elements of insignificant extra-solution activity and mere instructions to apply an exception are not indicative of integration into a practical application. Even when considered in combination, the additional elements do not provide an inventive concept, thus the claims are not eligible.
Claim 2 (and also claim 12) is dependent on claim 1 and therefore inherits the same judicial exception recited in claim 1. Claim 2 also recites operation of receiving, by the computer program, the subject from the large language model. This operation can reasonably be performed in the human mind, through observation, evaluation, judgement and opinion, with the aid of pen and paper, and therefore is reciting a mental process. The judicial exceptions recited in claims 1 and 2 are not integrated into a practical application because the mental processes are merely applied using a general-purpose computer, with the only additional elements comprising insignificant extra-solution activity. Claim 2 recites the additional element of providing, by the computer program, the text of the document to a large language model which is insignificant extra-solution activity of mere data gathering. These additional elements of insignificant extra-solution activity and mere instructions to apply an exception are not indicative of integration into a practical application. Even when considered in combination with the additional elements of claims 1-2, the additional elements do not provide an inventive concept, thus the claims are not eligible.
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.
Claims 1-2 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Santoso et al. (US 11,886471, filed 5/3/2021, hereinafter as Santoso) in view of Tsvetkov et al. (US 2024/0362412, filed 4/25/2023, hereinafter as Tsvetkov) in view of Kothari et al. (US 2017/0075528, published 3/16/2017, hereinafter as Kothari) in view of Gkoulalas (US 2019/0266353, published 8/29/2019).
Claim 1. A method for improving document readability, comprising:
receiving, by a computer program executed on a user electronic device a document comprising text, wherein the user electronic device comprises a display; Santoso discloses in [Abstract] “An electronic document specifying natural language text describing an issue with a complex system is received.” And in [col. 15, ln 52 to col. 16, ln 11] “the query processing component 114 could perform several different operations using the various sets of terms identified within the electronic documents (e.g., the highlighted terms shown in FIGS. 4A-D) and could perform a correlation operation (block 250) to reconcile the various results into a single aggregated set of results. Such a set of aggregated results could then be displayed in a graphical user interface, as shown in FIG. 5, which depicts a graphical interface representing exemplary query results, according to one embodiment of the present disclosure.” (emphasis added).
Santoso does not explicitly disclose
identifying, by the computer program, a subject for the document, wherein the identification includes a classification of the document. However, Tsvetkov, in an analogous art, teaches in [0012] “an organization has various client interactions that are memorialized in different types of content, such as emails, voicemails, conference call recordings, written notes from customer interactions or sales calls, or the like. Such content types contain natural language content that may natively exist in digital text form (e.g., text emails, text messages, customer interaction log files) or that may be converted into digital text form (e.g., voice content such as voicemails or conference call recordings). This text content can then be analyzed for important words or phrases, also referred to herein as “key words” or “key phrases,” which can then be used to index and organize the various content items, summarize content of text, and identify the main topics discussed.” And in [0013] “There are several known key-phrase extraction algorithms that attempt to identify such key phrases within text content. One family of key-phrase extraction methods identifies key phrases based on semantic similarity… Such algorithms rely upon phrases that are most similar to all of a text, and extract phrases that have the most semantic similarity to the text or are based on statistic and relationships between the words within the text.” And in [0043] “the key phrases may be displayed to the user 102 to allow the user 102 to identify what the content item 106 is about, see the main topics of the content item 106, the concepts in the content item 106, or the like.” (emphasis added) examiner note: text content may be analyzed for keywords or key-phrases for identifying topic or subject of the text content. Extracting/identifying similar key phrases for particular topic and/or concept may be classifying a document text by identifying the topic of the document as a document class.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Santoso with the teaching of Tsvetkov because “to index and organize the various content items, summarize content of text”. Tsvetkov [0012],
retrieving, by the computer program, a plurality of trigger words from a trigger word database specific to the subject and a plurality of skim-through words from a skim-through words database specific to the subject; further, Santoso discloses in [col. 10, ln 27-41] “FIG. 4B illustrates a document 420 where the query processing component 114 has analyzed the incoming document and has identified a set of terms (or keywords) within the document using a data dictionary. The identified terms are shown in bold, where terms such as “LEFT CAR DOOR TRIM” and “PLATING PROCESS” have been identified as potentially important terms within the document, based on the presence of these terms within the data dictionary… the identifying a first set of terms corresponding to the electronic document to include in the synthesized electronic document, using a data dictionary structure. For example, the data dictionary structure can specify a listing of words that are determined to be relevant terms, and the data dictionary structure may further specify a weight associated with each of the words that indicates a likelihood that the corresponding word is indicative of the discrepancy or fault represented by the electronic document. For example, a particular document may include several paragraphs of natural language text describing a discrepancy, but a particular phrase specifying that a specific part number has a visible crack could be especially meaningful as to the fault represented by the electronic document and the solution to the represented fault.” And in [col. 10, ln 42-51] “the query processing component 114 could identify a second set of terms corresponding to the electronic document to include in the synthesized electronic document, wherein at least one term in the second set of terms corresponding to satisfies at least one predefined pattern matching rule. For example, a pattern matching rule could be defined to recognize a format of part numbers used by a particular aircraft part supplier.” And in [col. 15, ln 21-51] “the machine learning model determined that terms such as “finish treatment” and “plating process” are relevant, while other terms such as “recorded lead time” are not. Furthermore, FIG. 4D illustrates the results of a pattern matching analysis on the received electronic document, with the identified terms appearing in bold and underlined font. For example, in the depicted embodiment, the query processing component 114 has determined that the part numbers “P/N XXXX1234” and “P/N XXXX2345” satisfy predefined pattern matching results for identifying part numbers following a particular naming convention and appearing within electronic documents.” (emphasis added) examiner note: the first and second sets of terms may be trigger words corresponding to an issue or fault as the subject of the electronic document, whereas terms such as “recorded lead time” may be not relevant or “skim-through words” in the electronic document as shown in figs 4A-C. The terms may be retrieved from data dictionary structure that may be structured discrepancy or fault represented in the document as the subject of the document,
identifying, by the computer program, trigger words in the text of the document based on the plurality of triggers from the trigger word database and applying a first graphical pattern to the identified trigger words; Santoso discloses in [col. 15, ln 21-51] “FIG. 4B illustrates a document 420 where the query processing component 114 has analyzed the incoming document and has identified a set of terms (or keywords) within the document using a data dictionary. The identified terms are shown in bold, where terms such as “LEFT CAR DOOR TRIM” and “PLATING PROCESS” have been identified as potentially important terms within the document, based on the presence of these terms within the data dictionary… the data dictionary can be constructed to include terms that are commonly indicative of the maintenance fault or discrepancy corresponding to the electronic document containing the terms… FIG. 4D illustrates the results of a pattern matching analysis on the received electronic document, with the identified terms appearing in bold and underlined font. For example, in the depicted embodiment, the query processing component 114 has determined that the part numbers “P/N XXXX1234” and “P/N XXXX2345” satisfy predefined pattern matching results for identifying part numbers following a particular naming convention and appearing within electronic documents.” (emphasis added) examiner note: relevant terms (trigger words) such as “finish treatment” and “plating process” may be indicated by bold and underlined font as graphical pattern, wherein the relevant terms may be present in a data dictionary as a database,
wherein the first graphical pattern visually brings the identified trigger words to a foreground of the document and comprises a high-contrast color and an increased font size; Santoso discloses in [col. 15, ln 21-51] “FIG. 4D illustrates the results of a pattern matching analysis on the received electronic document, with the identified terms appearing in bold and underlined font. For example, in the depicted embodiment, the query processing component 114 has determined that the part numbers “P/N XXXX1234” and “P/N XXXX2345” satisfy predefined pattern matching results for identifying part numbers following a particular naming convention and appearing within electronic documents.” (emphasis added) examiner note: fig. 4D may be an output of the pattern analysis on the received electronic document, wherein relevant terms such as “finish treatment” and “plating process” may be indicated by bold and underlined font and not relevant terms such as “recorded lead time” may be displayed in regular font and not bolded differentiating the important terms from the not important terms. Identified relevant terms (trigger words) such as “finish treatment” and “plating process” may be indicated by bold and underlined font, which may appear displayed in foreground relative to the white background as shown in fig. 4.
limiting, by the computer program, a number of identified trigger words in a paragraph of the text to a maximum value and, when more than the maximum value are identified, [selectively de-identifies certain words] as trigger words based on proximity constraints including no more than three consecutive trigger words and no more than three trigger words out of five words, wherein the maximum value is set based on machine learning; Santoso discloses in [col. 14, ln 60 to col. 15, ln 20 and col. 16, ln 12-36] “the phrase “shown in view/A/and B/” has been removed… The query processing component 114 then synthesizes the electronic document to create a synthesized electronic document, by removing one or more portions of the electronic document that are determined to satisfy one or more predefined filtering rules, identifying a first set of terms corresponding to the electronic document to include in the synthesized electronic document, using a data dictionary structure, and identifying a second set of terms corresponding to the electronic document to include in the synthesized electronic document, wherein each term in the second set of terms corresponding to satisfies at least one predefined pattern matching rule (block 620).” (emphasis added) examiner note: the term “limiting” may be interpreted as “removing” such that a portion (identified terms) of the electronic document may be removed based on predefined rules (proximity constraints including no more than three consecutive trigger words and no more than three trigger words out of five words),
Santoso does not explicitly disclose selectively de-identifies certain words. However, Gkoulalas, in an analogous art, teaches in [0013 and 0027] “when quasi-identifier attributes are used together, the combinations of values of the quasi-identifier attributes in the dataset may be unique for some individuals, leading to their re-identification. For example, the combination of values for attributes “five-digit zip code”, “gender”, and “date of birth” is unique for a large number of United States citizens, hence these attributes may be used together to issue successful re-identification attacks… De-identification module 170 applies de-identification providers to datasets in order to de-identify data. In particular, de-identification module 170 uses de-identification providers to protect direct and quasi-identifiers in datasets, so that they can no longer be used for performing re-identification attacks. Additionally, de-identification module 170 may protect sensitive attributes in a dataset, such as medical history or salary, from being accurately mapped to the corresponding individuals. De-identification module 170 may apply one or more de-identification providers that are selected by provider evaluation module 160 or by a user. De-identification module 170 may output de-identified data to storage 180 or database 120.” (emphasis added) examiner note: the certain words that may be de-identified, by the de-identification module 170, may be personally identifiable information (PIT) for security purposes.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Santoso with the teaching of Gkoulalas because “Data de-identification or anonymization refers to the practice of transforming features in data in order to prevent a person's identity from being linked to other information about the person. Data is commonly de-identified in order to make use of the data while preserving the privacy of individuals. For example, medical data may be de-identified before it is used for clinical research purposes. Data utility, or usefulness of the data, generally decreases as that data is further de-identified. Thus, there is typically a balance between data privacy and data utility.” Gkoulalas [Background].
identifying, by the computer program, skim-through words in the text of the document based on the plurality of skim-through words from the skim-through words database and applying a second graphical pattern to the identified skim-through words, wherein the skim-through words include prepositions; Santoso, further, discloses in [col. 14, ln 60 to col. 15, ln 20] “FIG. 4A illustrates a document 400 where a number of stop words and inconsequential phrases have been removed by a preprocessing operation. For example, the words “of”, “the”, “that” “ ” and so on have all been removed from the document, as these stop words are commonly used across the majority of documents and provide little semantic meaning to the document in question. Additionally, the phrase “shown in view/A/and B/” has been removed. In the present example, assume that a regular expression was created to remove this (and similar) phrases, as these phrases alone add little to the semantic meaning of the document (e.g., the phrase merely refers to the attached images, and adds no value in and of itself).” (emphasis added) examiner note: the words “of”, “as”, and “to” may be skim-words that include prepositions indicated by strike-through graphical patterns as shown in fig. 4A,
wherein the second graphical pattern visually brings the identified skim-through words to a background of the document and comprises a low-contrast color and a decreased font size; Santoso discloses in [col. 15, ln 21-51] “FIG. 4D illustrates the results of a pattern matching analysis on the received electronic document, with the identified terms appearing in bold and underlined font. For example, in the depicted embodiment, the query processing component 114 has determined that the part numbers “P/N XXXX1234” and “P/N XXXX2345” satisfy predefined pattern matching results for identifying part numbers following a particular naming convention and appearing within electronic documents.” (emphasis added) examiner note: fig. 4D may be an output of the pattern analysis on the received electronic document, wherein relevant terms such as “finish treatment” and “plating process” may be indicated by bold and underlined font and not relevant terms such as “recorded lead time” may be displayed in regular font and not bolded differentiating the important terms from the not important terms. Identified non-relevant terms (skim-through-words) such as “recorded lead time” may be indicated by regular un-bolded font, which may appear displayed in background relative to the bolded, underlined font of relevant terms as shown in fig. 4. The limitations “low-contrast color” and “decreased font size” may be interpreted as variants of the font properties to distinguish the important terms, non-important terms, stop words, etc. and Santoso teaches the use of font properties such “bold” and “underline” in order to differentiate the terms in the document,
Santoso does not explicitly disclose
determining, by the computer program, that a percentage of the identified trigger words in at least a portion of the document is [below a threshold], wherein the determining comprises comparing a number of the identified trigger words in the portion to a total number of words in the portion. However, Kothari, in an analogous art, discloses in [0087-0091] “parsing the full data file, and selecting individual words or phrases to “tag” the data file, with a goal to select whole sentences to create a short paragraph summary at operation 602… The method (700) comprises opening the data file at operation 701, analyzing a structure of the text to determine key phrases that appear central to the text and summarizing the same at operation 702, and displaying the data on the data pane at operation 703… Referring to FIG. 8, a sample summarization method (800) using cohesive summary extraction is illustrated. The method (800) comprises opening the data file at operation 801. The full data file is parsed and important sentences are extracted at operation 802. Extraction of the important sentences can be based on one or more of: features—sentence location, cardinality, title similarity, keywords, learner-dependent readability-related features such as average sentence length, percentage of trigger words, percentage of polysyllabic words, and percentage of noun entity occurrences. In an optional embodiment, a number of extracted words can be limited based on a user requirement at the time of summarizing at operation 803 and the data is displayed on the tab pane at operation 804.” (emphasis added) examiner note: the percentage of trigger words may indicate the threshold value for summarizing the open data file. Let’s assume, the percentage of the trigger words is 5, then the system would summarize the data file and display the summary of the data file in a in the browser tab pane. However, if the percentage of trigger words is less than the threshold value, let’s say 4, then the system would not summarize the data file and the output would be no word is to be tagged or changed. The extraction of important sentences based on percentage of trigger words may indicate ratio (comparing) the number of trigger words to the total number of words in the document/portion.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Santoso with the teaching of Kothari because using emotional trigger words, for example, can significantly impact responses.
outputting, by the computer program to the display, the document with the identified trigger words output in the first graphical pattern, and the skim-through words being output in the second graphical pattern and based on the threshold, wherein words in the text that are not identified trigger words and are not identified skim-through words are output in a third graphical pattern, wherein the third graphical pattern is a neutral graphical pattern, and wherein the third graphical pattern is selected responsive to backgrounds of the document and graphical properties of core words based on the subject. Santoso discloses in [col. 15, ln 21-51] “FIG. 4A illustrates a document 400 where a number of stop words and inconsequential phrases have been removed by a preprocessing operation. For example, the words “of”, “the”, “that” “ ” and so on have all been removed from the document, as these stop words are commonly used across the majority of documents and provide little semantic meaning to the document in question. Additionally, the phrase “shown in view/A/and B/” has been removed… FIG. 4D illustrates the results of a pattern matching analysis on the received electronic document, with the identified terms appearing in bold and underlined font. For example, in the depicted embodiment, the query processing component 114 has determined that the part numbers “P/N XXXX1234” and “P/N XXXX2345” satisfy predefined pattern matching results for identifying part numbers following a particular naming convention and appearing within electronic documents.” (emphasis added) examiner note: fig. 4D may be an output of the pattern analysis on the received electronic document, wherein relevant terms (trigger words) such as “finish treatment” and “plating process” may be indicated by bold and underlined marking shown in fig. 4B-4C and stop words (skim-through words) as being common words across the majority of documents as they are indicated by strike-through marking shown in fig. 4A. A third group words and/or phrases identified as not relevant such as “recorded lead time” may be displayed in regular font and not bolded differentiating the phrase from important terms (trigger words) and the stop words as they are common across the majority of documents.
Claims 2 and 12. The rejection of the method of claim 1 is incorporated, wherein the step of identifying the subject for the document comprises:
Santoso does not explicitly disclose providing, by the computer program, the text of the document to a large language model; and receiving, by the computer program, the subject from the large language model. However, Tsvetkov, in an analogous art, teaches in [0012] “This text content can then be analyzed for important words or phrases, also referred to herein as “key words” or “key phrases,” which can then be used to index and organize the various content items, summarize content of text, and identify the main topics discussed.” And in [0043] “the KP engine 110 uses the key-phrase indices of the processed content items 106 for content operations such as search, clustering, or the like. In some implementations, the key phrases may be displayed to the user 102 to allow the user 102 to identify what the content item 106 is about, see the main topics of the content item 106, the concepts in the content item 106, or the like.” And in [0039-0040] “the operations of flowchart 500 are performed by the KP engine 110 using the LLM 120 of FIG. 1. The content items 106 can be any type of digital content that contains natural language content, such as documents, audio, video, or the like. At operation 510, the KP engine 110 identifies a subject content item 106, such as document 108… the subject content item 106 contains native text content (e.g., text content 210) that can be directly evaluated by the KP engine 110. In some situations, the content item 106 contains text content, but the text content may not be directly usable. For example, text content 210 is extracted from a word processing document, optical character recognition (OCR) content embedded in a PDF document, or web page content embedded within tags of a web page.” (emphasis added) examiner note: the KP engine 110 using LLM 120 may identify topic or subject of input document 108.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Santoso with the teaching of Tsvetkov because “to index and organize the various content items, summarize content of text”. Tsvetkov [0012].
Claim 11. The claim is directed toward a system for implementing the method of claim 1, therefore, the claim is similarly rejected as claim 1. Further, Santoso discloses a trigger word database comprising a plurality of trigger words; a skim-through database comprising a plurality of skim-through words; Santoso discloses in [col. 10, ln 27-67] “the identifying a first set of terms corresponding to the electronic document to include in the synthesized electronic document, using a data dictionary structure. For example, the data dictionary structure can specify a listing of words that are determined to be relevant terms, and the data dictionary structure may further specify a weight associated with each of the words that indicates a likelihood that the corresponding word is indicative of the discrepancy or fault represented by the electronic document…the query processing component 114 could identify a second set of terms corresponding to the electronic document to include in the synthesized electronic document, wherein at least one term in the second set of terms corresponding to satisfies at least one predefined pattern matching rule. For example, a pattern matching rule could be defined to recognize a format of part numbers used by a particular aircraft part supplier… The query processing component 114 could then access an index for a data repository using the synthesized electronic document to identify a first set of relevant electronic documents within the data repository. For example, each document within the data repository could be indexed based on an evaluation of the contents of the respective document in view of the data dictionary structure as well as the predefined pattern matching rules.” (emphasis added).
an electronic device comprising: a display; a computer processor; and a memory storing a document readability computer program comprising instructions, Santoso discloses in [col. 7, ln 25-41] “the system 100 includes a query analysis system 105, a plurality of aircraft 135 and a customer system 170, interconnected by a data communications network 130. The query analysis system 105 includes, without limitation, one or more computer processors 110, a memory 112, and storage 120, each connected to a bus (not shown). The query analysis system 105 may also include an input/output (I/O) device interface (not shown) connecting I/O devices (e.g., keyboard, mouse, and display devices) to the query analysis system 105.” (emphasis added).
Response to Arguments
Applicant’s arguments with respect to claims 1 and 11 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Argument: with regard to the 35 USC 101 claim rejections, Applicant argues “The amended claim 1 overcomes the § 101 rejection because it is directed to a practical application, an improved computer graphical user interface, implemented through specific, technical display-control operations that cannot be performed in the human mind. The claim now requires the program to render three coordinated visual layers with precise display rules: trigger words are emphasized in the foreground via high-contrast color and increased font size (SPEC, step 220: [0044]); skim-through words, including prepositions, are deemphasized into the background via low-contrast color and decreased font size (SPEC, step 225: [004 7]); and all remaining core words are shown in a neutral third pattern whose selection is responsive to document backgrounds, while core word graphical properties remain subject-dependent and unchanged (SPEC, steps 230 and background responsiveness: [0048]-[0050]). The claim further implements rule-based highlight density control by limiting the number of identified trigger words per paragraph to a maximum value, selectively de-identifying based on proximity constraints ( e.g., no more than three consecutive trigger words and no more than three out of five), with the maximum set via machine learning (SPEC, [0045]-[0046]). It additionally gates modifications using a per-portion threshold comparison by determining whether the percentage of identified trigger words in a portion falls below a threshold through a comparison of identified trigger words to total words, and, when below the threshold, outputs "no changes" for that portion (SPEC, steps 235-245: [0052]-[0054]). These elements are integrated into the solution, not incidental pre- or post-solution activity, because the outputting defines how the device renders text to create scannable emphasis layers (SPEC, [0027], [0031 ]), and the adaptive selection responsive to background and proximity-constrained density control improve readability by avoiding visual clutter (SPEC, [0045]-[0050]).”
Response: emphasizing and deemphasizing words in text document are operations can be performed in the human mind through observation, evaluation, judgment, and opinion and the help of using general-purpose computer.
Argument: with respect to the art rejection based on 35 USC 103, Applicant argues “the claim's rule-based highlight-density limiting and proximity constraints, with a machine learning-based maximum, are absent from the cited art. The claim requires "limiting ... a number of identified trigger words in a paragraph ... to a maximum value and, when more than the maximum value are identified, selectively de-identifies certain words as trigger words based on proximity constraints including no more than three consecutive trigger words and no more than three trigger words out of five words, wherein the maximum value is set based on machine learning." Santoso teaches identifying "relevant" terms via a data dictionary and pattern matching and making those terms more salient in the synthesized document (col. 10, lines 27-51; col. 15, lines 21-51; FIGS. 4B-4D). Santoso' s pipeline is designed to emphasize and retain relevant terms for query synthesis, not to de-emphasize or "de-identify" some of those terms when local density is high.” (emphasis added).
Response: Santoso teaches in [col. 11, ln 36-53] “the query processing component 114 can utilize pattern matching to identify when a plurality of words satisfy one or more predefined matching rules.” The amendment of “de-identification of words” has been addressed using new cited prior art as detailed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHAMED I NAZAR whose telephone number is (571)270-3174. The examiner can normally be reached 10 am to 7 pm Mon-Fri.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Stephen Hong can be reached at 571-272-4124. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AHAMED I NAZAR/Examiner, Art Unit 2178 4/3/2026
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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