DETAILED ACTION
This office action is in response to Applicant’s Request for Continued Examination (RCE), received on 12/31/2025. Claims 1 and 11 have been amended. Claims 1-2, 4-12, 14-20 are pending and have been considered.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/31/2025 has been entered.
Response to Arguments
Applicant's arguments filed 12/31/2025, see pgs. 8-10, with respect to “Claim Rejections Under 35 U.S.C. 101” have been fully considered but they are not persuasive.
Applicant’s representative asserts, “Claims 1-2, 4-12, and 14-20 stand rejected under 35 U.S.C. § 101 as allegedly being directed to an abstract idea without significantly more. See Office Action at 11. Applicant respectfully disagrees and respectfully submits that the claimed embodiments recite statutory subject matter and are patentable over § 101.
The 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence ("Guidance"), sets forth a subject matter eligibility test. The first prong (Step 2A, Prong One) of the USPTO's subject matter eligibility analysis is a determination of whether a claim recites a judicial exception. As explained in MPEP 2106.04, subsection II.A.1:
a claim ‘recites’ a judicial exception when the judicial exception is ‘set forth’ or ‘described’ in the claim. If the claim does not recite a judicial exception, it is considered eligible, and the eligibility analysis ends. But if the claim does recite a judicial exception, the eligibility analysis continues to the second prong of Step 2A.
Even if the claims did recite the alleged abstract idea (which Applicant is not conceding), the claims integrate any abstract idea into a practical application. At Step 2A-Prong Two, the analysis must consider the claims as a whole; that is, all recited elements need to be evaluated together with the judicial exception. See, e.g., October 2019 Update: Subject Matter Eligibility at 12; MPEP § 2016.04(d)(III). Applicant respectfully submits that when the proper analysis is performed, and the claims are reviewed as a whole, it can be seen that the claims integrate any abstract idea into a practical application. Thus, Applicant respectfully submits that even if the claims recite an abstract idea (which Applicant does not concede), the claims recite significantly more. Indeed, the claims are focused on a specific embodiment that is integrated a practical application and does not preempt all applications in this area.
MPEP 2106.05(d)(I) states that, in determining patent eligibility, examiners should consider whether the claim offers a technological solution to a technological problem. To that end, the present embodiments clearly offers a solution to the following technological problem, as detailed in the specification at paragraphs [0032] and [0033] regarding the innovative platform according to exemplary embodiments. Paragraph [0035], for example, describes the technological solution that the claimed embodiments present for this identified problem using different technologies such as machine learning. Also, paragraphs [00176], [00177], [00198], and [00201], for example, describe the solution of the claimed embodiments and its technology. Therefore, the claims integrate any abstract idea into a practical application. The claims have further been amended as shown above to address the obviousness rejection, as discussed below. Applicant respectfully submits that these amendments are equally applicable to the § 101 rejection and further show that the claims recite patentable subject matter.
Here, the Office has alleged that the claims cover nothing more than a mental process. Office Action at 12-13. Applicant respectfully disagrees. In reaching its conclusion, the Office merely takes the claims and simplifies the elements, without considering the problems solved by the claimed embodiments as noted above. The claims recite elements that go beyond simple mental processes.
And, building upon the reasoning above, one of ordinary skill would appreciate the claimed embodiments, particularly as amended, go beyond anything that can be done as a mere mental process or even using pen and paper. The claimed embodiments are therefore beyond mere mental processes as alleged. Applicant respectfully submits that amended claims serve to further refine the claimed embodiments and demonstrate the claims integrate any abstract idea (to the extent one is even recited) into a practical application and also offer a technological solution to a technological problem as described in the specification and admitted by the Office. For example, the claims take an input (one or more guidelines that are mortgage industry specific) and transform it, ultimately outputting executable base code snippets. This demonstrates the technological solution presented by the claimed embodiments.
Accordingly, Applicant respectfully submits that claims 1-2, 4-12, and 14-20 satisfy the requirements of 35 U.S.C. § 101, and respectfully requests withdrawal of the rejection.”
In response, the examiner would like to refer to the currently amended claim language in view of Applicant’s cited claimed improvements. Specifically, the examiner respectfully disagrees with Applicant’s assertions that “the present embodiments clearly offers a solution to the following technological problem” (see Applicant remarks, pg. 9) and “one of ordinary skill would appreciate the claimed embodiments, particularly as amended, go beyond anything that can be done as a mere mental process or even using pen and paper” (see Applicant remarks, pg. 10). The cited paragraphs of the specification disclose “An embodiment of the present invention is directed to a data-driven, analytics-based system that increases efficiencies and reduces costs for all stakeholders…leveraging Artificial Intelligence (AI) and Machine Learning (ML) technologies…” ([0033]), “Operational efficiencies may be improved across the platform as blockchain technology, artificial intelligence, and machine learning eliminate or substantially reduce inefficient and arcane processes…” ([0035]), “An embodiment of the present invention is directed to leveraging natural language processing (NLP) technology based in advanced machine learning/deep learning technologies to create an improved method and system for extracting rules from guidelines” ([00198]). All of these cited sections are relying upon artificial intelligence and/or machine learning as the means of improvement. The examiner respects Applicant’s disclosure that this is an improvement to the technology, but it is unclear to the examiner how this improvement can be incorporated into the claim when no artificial intelligence or machine learning technology is claimed. The “natural language processing engine” as currently claimed does not require an interpretation of this component to be AI or ML, nor are any of the following claim steps related to AI, ML, or blockchain technology; therefore, an improvement brought upon by ML and/or AI technology cannot be introduced without the required technology to be improved upon.
Similarly, [00176] discloses a knowledge graph “With such magnitude of unstructured data being available in the form of guidelines/directions/instructions pertaining to the mortgage domain, there is a vital need to not only store this data but to also connect the information”. The examiner respects that the knowledge graph is designed to have such a magnitude of data as to prevent an interpretation of generation of the knowledge graph from being a mental process assisted with pen and paper, but there is nothing in the claim which requires the amount of data being processed and generated into the knowledge graph to be at such a level to preclude a mental process interpretation of the knowledge graph, i.e. that written on paper. The example guidelines provided in Fig. 6 of the instant application are in the mortgage industry, but are not at a complexity which prevents a user from being able to physically create a knowledge graph representing the text.
The examiner respectfully asserts that [0032], [00177], and [00201] do not incorporate or disclose any improvements to technology claimed. For example, [0032] discloses improving servicing of mortgage loans. Mortgage loan servicing is not claimed. [00177] merely claims applying a weight-based approach in preparing a node-tree structure and keeping hierarchy intact. This is not tied to an improvement. [00201] discloses parsing documents into three formats, all of which are mental processes as respectfully asserted by the examiner. No improvements to the process of the parsing operation are cited, nor claimed. See updated rejections below.
Applicant’s arguments, see pgs. 10-16, filed 12/31/2025, with respect to “Claim Rejections Under 35 U.S.C. 103” have been fully considered and are persuasive. The rejections of claims 1-2, 4-12, 14-20 have been withdrawn. See statement of reasons for allowance below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-2, 4-12, 14-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent claim(s) 1, 11 recite:
receiving, via an interface, an input data comprising one or more guidelines that are mortgage industry specific, wherein the interface communicates with one or more client systems via a communication network;
extracting, via a natural language processing engine that comprises a computer processor, a plurality of text segments from the input data using a verb base approach wherein at least one verb forms a root of each text segment;
labeling, via the computer processor, each of the plurality of text segments with a depth-parameter number based on a weight-based approach wherein the weight-based approach determines a weight that represents text relevance using a set of factors based on text size, text weight, indent and text format;
generating, via the computer processors, a node-tree structure that represents the weight for each of the plurality of text segments, wherein the node-tree structure organized the plurality of text segments in a hierarchical arrangement based on the depth-parameter numbers;
applying, via the computer processor, the weight-based approach within a predetermined scope as defined by a data-dictionary and a knowledge graph, wherein the data-dictionary and knowledge graph are specific to a mortgage domain;
generating a flow chart based on the weight-based approach and the hierarchical arrangement of the node-tree structure, wherein the flow chart represents a transformation of the plurality of text segments into illustrative representations and captures a flow of all required condition-action relationships identified from the hierarchical node-tree structure; and,
generating a code base corresponding to the flow chart and comprising base code snippets as individual functions based on application of the weight-based approach, wherein the code base is executable to implement the condition-action relationships.
These limitations, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, the claim(s) read(s) on receiving input data, i.e. text, comprising one or more guidelines, i.e. statements, extracting text segments from the input based on rooting each text to a verb contained within the text, labelling the text segments based on a weight determined through visual appearance and/or content, generating a tree to combine the text with weights wherein the tree is arranged in a hierarchical fashion based on numbers assigned to the segments, applying the aforementioned approach within a specific scope using a predefined data dictionary or knowledge graph, generating a flow chart based on the weight-based approach which represents the condition-action relationships identified from the tree, and generating a code base the implements the condition-action relationships. That is, other than reciting "natural language processing engine" (Claims 1, 11), "computer processor" (Claims 1, 11), "interface" (Claims 1, 11), "memory component" (Claim 1), nothing in the claim element precludes the step from practically being performed in the mind. All of these steps can be performed in the mind and/or using pen and paper.
For example, consider being given an annotated/edited document to be reviewed. A user will be provided with the text document which can be physically written on paper, i.e. receive an input data comprising one or more guidelines, wherein guidelines represent text strings/sections in view of Fig. 6: 144 represents a title, i.e. not a specific if-action statement (compared to 145 and 146), indicating any text can be a guideline. Limiting these guidelines to a mortgage domain does not prevent them from being written on paper to be presented and mentally analyzed. A user can then extract, i.e. write down, sentences, i.e. text segments, containing verbs, i.e. a verb based approach, from the input text, i.e. written document, wherein the user can also write the verb separately from the sentence, indicating it as a “root” of the text segment. Labelling text segments with a depth-parameter based number based on a weight-based approach wherein the weight is dependent on text size, weight, indent, and format is a mental process consisting of labelling text with a generic numbers determined through an arbitrary calculation. As reading, a user will identify bolded (format), larger (size), relevant (weight), words or segments of text within a larger body of text through the mental process of visual identification and/or reading comprehension. Determining if text is indented is a similar process. Taking these determinations to develop a weight for the associated text is an arbitrary calculation which can be determined mentally using any set of numbers/calculations the user desires to determine a weight, i.e. if the text has a bold word, increase score by one. If indented, increase by 0.5. If the font is larger, increase score. If the text contains words relevant to the rest of the document, e.g. “mortgage”, increase the score. Finally, find the average of these scores. A simple addition and/or division is something that can be performed mentally. Generating a node-tree structure that represents the weight for each of a plurality of text segments can be something written on paper as the individual text segments with associated scores in an outline format, see Fig. 6, 620 and consider how a similar document could be hand-written. Determining which topics are subtopics of a heading and/or larger topic (as represented through the depth-parameter numbers) and organizing entries based on numbers associated with entries is a mental process, i.e. format the organization to have the smallest numbers at the top and the largest numbers at the bottom. Applying the previously determined mental process to a predetermined scope as defined by a data-dictionary and knowledge graph is limiting a mental process to a specific environment. This does not provide an inventive step. Further, a user could be presented with a physical manual of common words for specific areas, i.e. data-dictionary, and associated topics between those words/areas, i.e. knowledge graph, leading to a change in how the weighting is performed, but does not preclude that weighting from remaining a mental process. Limiting the data dictionary and knowledge graph to a mortgage domain does not prevent the functionality/creation of these objects from being performed mentally with the aid of pen and paper. Generating a flow chart representative of text segments into visualizations capturing condition-action relationships can be written on a piece of paper. Gathering these relationships from a hierarchical node-tree (which can similarly be drawn on paper with pen) indicates a mental process of reading the tree and creating flow chart operations associated with the reading of the tree. The user can write the process for completing the mortgage (or any other) document in the form of a flow chart based on what the process is as gathered from the hierarchical tree linking numbers (weights) to segments of text. Further, code can also be written using pen and paper and the generation of code based on the flowchart is equivalent to a mental comparison/translation of these two written pieces of information. Writing the code from a physical document containing condition-action relationships is the mental process of translating raw text into code as can be written on paper to be transferred to a computing system.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea (Step 2A, Prong one, Yes).
This judicial exception is not integrated into a practical application because the addition of generically recited computer elements does not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claims are directed to an abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception ( Step 2A, Prong two, No). As discussed above, with respect to integration of an abstract idea into a practical application, the additional element of "receive", "extract", "label", "generate", "apply" are merely for the purpose of data gathering, storing, processing, and/or insignificant extra-solution activity that amount to no more than mere instructions to apply the exception using a generic computer component. Paragraph(s) 3 of the instant application disclose(s) applying the method to a generic computing device such as a PC (Computers, laptops, etc.). Mere instructions to apply the exception using a generic computer component cannot provide an inventive concept. Therefore, the claims are not patent eligible (Step 2B, No).
Similarly, dependent claim(s) 2, 4-10, 12, 14-20 include additional steps that are considered “insignificant extra-solution activity to judicial exception” because they fail to provide meaningful significance that goes beyond generally linking the use of an abstract idea to a particular technological environment.
For example, claim 2 reads on determining a subject-verb-object structure for each text segment, identifying a condition-action relationship for each text segment, and identifying how the condition-action relationships are interlinked to other text segments, wherein the relationships are used to develop a hierarchy. Determining subject-object-verb relationships between words in text segments is a mental process associated with reading comprehension. Reading text and determining that there is an action to be made based on a condition associated with the text is also a mental process associated with reading comprehension. Identifying connections between various actions/conditions within text segments is also a mental process associated with linking common ideas through reading comprehension. Developing a hierarchy to represent these connections can be done on paper with physical writing, drawing lines between text segments explaining connections.
Claim 4 reads on font size defining the overall text hierarchy. Ordering text based on font size is a mental process associated with visual identification of font size. Larger texts can be written towards the top of a document, descending down the page with font size.
Claim 5 reads on text weight representing one or more of bold, italics, and highlighted text. Determining to weight text based on the text being bold, italics, and/or highlighted is a mental process associated with reading/visual identification.
Claim 6 reads on indent representing a text configuration feature. Determining to indent text based on weighting and/or relevance compared to other sections of text is a mental process. Consider note taking in an outline format, indentation is a natural, mental process associated with statement/topic b differentiation.
Claim 7 reads on the text format representing an uppercase format. Determining to use uppercase text as a means of format to determine weight is a mental process associated with reading. A user will mentally be able to determine capitalized words or phrases.
Claim 8 reads on using a human in the loop feature for feedback to improve accuracy. Presenting a generated flow of text organized based on weight for feedback is a mental process assisted with pen and paper. The generated, i.e. written, flow can be presented to a user and their feedback can be written on the same piece of paper they were originally presented with. Further, it is unclear to the examiner what accuracy is being improved and what the feedback is based on. For these reasons, it is unclear how the addition of human feedback provides an advantage to this specific invention different from standard human feedback techniques well known in the art before the effective filing date of the claimed invention.
Claim 9 reads on the guidelines comprising the input text being associated with one or more mortgage government agencies. Specifying the input text to an environment does not change the fact that the operations performed on the text are mental. The text is just being limited to a specific environment which does not provide an inventive concept.
Claim 10 reads on the natural language processing engine being applied to a plurality of domains with different weights applied based on the domain. Determining different weighting schemes based on the domain is an arbitrary determination which can be performed mentally, i.e. if the domain is intellectual property, i.e. an office action, italicized/bold text should be prioritized as the mappings to prior art are the relevant pieces of information, with font size not as relevant. A mortgage domain might have a more significant weighting factor on font size and/or a completely different corpus of text for determining relevance, but any differentiating factor can be determined mentally.
Claim 12 reads on determining a subject-verb-object structure for each text segment, identifying a condition-action relationship for each text segment, and identifying how the condition-action relationships are interlinked to other text segments, wherein the relationships are used to develop a hierarchy. Determining subject-object-verb relationships between words in text segments is a mental process associated with reading comprehension. Reading text and determining that there is an action to be made based on a condition associated with the text is also a mental process associated with reading comprehension. Identifying connections between various actions/conditions within text segments is also a mental process associated with linking common ideas through reading comprehension. Developing a hierarchy to represent these connections can be done on paper with physical writing, drawing lines between text segments explaining connections.
Claim 14 reads on font size defining the overall text hierarchy. Ordering text based on font size is a mental process associated with visual identification of font size. Larger texts can be written towards the top of a document, descending down the page with font size.
Claim 15 reads on text weight representing one or more of bold, italics, and highlighted text. Determining to weight text based on the text being bold, italics, and/or highlighted is a mental process associated with reading/visual identification.
Claim 16 reads on indent representing a text configuration feature. Determining to indent text based on weighting and/or relevance compared to other sections of text is a mental process. Consider note taking in an outline format, indentation is a natural, mental process associated with statement/topic b differentiation.
Claim 17 reads on the text format representing an uppercase format. Determining to use uppercase text as a means of format to determine weight is a mental process associated with reading. A user will mentally be able to determine capitalized words or phrases.
Claim 18 reads on using a human in the loop feature for feedback to improve accuracy. Presenting a generated flow of text organized based on weight for feedback is a mental process assisted with pen and paper. The generated, i.e. written, flow can be presented to a user and their feedback can be written on the same piece of paper they were originally presented with. Further, it is unclear to the examiner what accuracy is being improved and what the feedback is based on. For these reasons, it is unclear how the addition of human feedback provides an advantage to this specific invention different from standard human feedback techniques well known in the art before the effective filing date of the claimed invention.
Claim 19 reads on the guidelines comprising the input text being associated with one or more mortgage government agencies. Specifying the input text to an environment does not change the fact that the operations performed on the text are mental. The text is just being limited to a specific environment which does not provide an inventive concept.
Claim 20 reads on the natural language processing engine being applied to a plurality of domains with different weights applied based on the domain. Determining different weighting schemes based on the domain is an arbitrary determination which can be performed mentally, i.e. if the domain is intellectual property, i.e. an office action, italicized/bold text should be prioritized as the mappings to prior art are the relevant pieces of information, with font size not as relevant. A mortgage domain might have a more significant weighting factor on font size and/or a completely different corpus of text for determining relevance, but any differentiating factor can be determined mentally.
Therefore, these claims are also not patent eligible.
Allowable Subject Matter
Claims 1-2, 4-12, 14-20 are allowed over the prior art. Claims 1-2, 4-12, 14-20 still remain rejected under 35 U.S.C. 101 as ineligible subject matter directed to an abstract idea in the form of a mental process aided with pen and paper. These rejections need to be overcome for issuance of a patent for the allowable claims. Amendments to resolve outstanding issues under 35 U.S.C. 101 may affect the scope of the claims and may require reassessment of the amended claims for allowability.
The following is an examiner’s statement of reasons for allowance:
Considering claim 1, the closest prior art of record is Maheswaran et al. (US-20210319481-A1), hereinafter Maheswaran, Lao et al. (US-11538210-B1), hereinafter Lao, Mwarabu et al. (US-20200342053-A1), hereinafter Mwarabu, Kanchibhotla et al. (US-20220405484-A1), hereinafter Kanchibhotla, Shyamsundar et al. (US-20170315714-A1), hereinafter Shyamsundar, Danielyan (US-20180081861-A1), Ziuzin et al. (US-20200342059-A1), hereinafter Ziuzin, and Park et al. (US-20210109960-A1), hereinafter Park.
Maheswaran discloses: a system that implements a natural language processing engine ([0021] using natural language processing models), the system comprising:
an interface that communicates with one or more client systems via a communication network ([0022] The customer interface 50 and the customer interaction subsystem 20 communicate via a network [Referring to Fig. 1, it can be seen that the customer interface 50 and interaction subsystem 20 are distinct, with the interaction subsystem 20 receiving information from the customer, indicating the interaction subsystem is a client system]); and
a natural language processing engine comprising a computer processor coupled to the interface and a memory component ([0036] The memory 220 includes a plurality of subsystems stored in the form of executable program which instructs the processor 210 to perform the method steps illustrated in FIG. 1 [Where Fig. 1 contains interface 50, indicating a natural language processing engine comprising a system where the processor is coupled to the interface, i.e. processes information entered on/through interface]), the computer processor further configured to:
receive, via the interface, an input data comprising one or more guidelines ([0021] the customer interaction subsystem 20 may receive the input corpus 30 from the interaction of the customers 40 with the customer interaction subsystem 20…[0022] the customers 40 may interact with the customer interaction subsystem 20 via a corresponding customer interface 50 [In view of Fig. 6 of the instant application: section 144 represents a title, i.e. not a specific if-action statement (compared to 145 and 146), indicating any text can be a guideline. Further in view of the example input corpus 30, see [0030], indicating a guideline to not return diapers when the packaging is opened]); and,
extract, via the computer processor, a plurality of text segments from the input data using a verb base approach where at least one verb forms a root of each text segment ([Fig. 4, 150], [0032] The phrase extractor subsystem 110 identifies one or more root verbs such as “leaking” 150 in the input corpus 30 and the relation between each token 80 [Extracting phrases is an equivalent action to extracting text segments, in view of the input corpus 30 representing a plurality of interactions ([0021])]).
Maheswaran does not disclose:
label, via the computer processor, each of the plurality of text segments with a depth-parameter number based on a weight-based approach wherein the weight-based approach determines a weight that represents text relevance using a set of factors based on text size, text weight, indent and text format; and,
generate, via the computer processors, a node-tree structure that represents the weight for each of the plurality of text segments,
wherein the node-tree structure organizes the plurality of text segments in a hierarchical arrangement based on the depth-parameter numbers.
Lao discloses:
label, via the computer processor, each of the plurality of text segments with a depth-parameter number based on a weight-based approach wherein the weight-based approach determines a weight that represents text relevance using a set of factors based on text size, text weight, indent and text format ([Fig. 2, Features Vectors 132, Text Importance Vector 120], [Col. 7, Lines 40-50] The text layout manager can then receive a text importance vector 120 that includes vector elements 122 as designations of visual properties 124 for the constituent words 118 of the text phrase 116. The visual properties 124 for the constituent words 118 of the text phrase can include a text size, a font style, a color of the constituent words, or any other type of visual property, [Col. 7, Lines 34-37] a text phrase with even a few words can quickly expand to near infinite possible combinations of word size, word spacing, and overall layout, [Col. 8, Lines 60-67] The text layout manager 104 can assign weights to the text objects to represent a relative importance of the individual constituent words 118 within the sequence of text objects, where a greater weight corresponds to a greater relative importance of a word. Alternatively or in addition, weights can be assigned to the text objects based on styles of the constituent words 118 as described by the text input [In view of the feature vectors 132 of Fig. 2, it is apparent that text segments, i.e. words/series of words, are labelled with weights based on visual formatting including text size, text format, i.e. font, text weight, i.e. text importance vector 120, and indent, i.e. word spacing, and/or overall layout of text, through the use of feature vectors]); and,
generate, via the computer processors, a node-tree structure that represents the weight for each of the plurality of text segments ([Col. 8, Lines 53-55] The binary tree structure provides data representation and text weighting for laying out the constituent words 118 of the text phrase 116 in the various spatial layouts 130, [Col. 9, Lines 13-17] The text layout manager 104 can generate a binary tree representation of a spatial layout 130, where the binary tree has leaf nodes that represent the constituent words 118 and parent nodes that define relative alignment or orientations of the words [In view of the plurality of text segments of Maheswaran]),
wherein the node-tree structure organizes the plurality of text segments in a hierarchical arrangement based on the depth-parameter numbers ([In view of the previously disclosed text feature vectors and binary tree defining alignment or orientations of words indicating the alignment, i.e. arrangement, within the tree to be based on the depth-parameters numbers, i.e. the components of the feature vectors]).
Maheswaran and Lao are considered analogous art within semantic analysis of text (G06F40/30). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Maheswaran to incorporate the teachings of Lao, because of the novel way to account for visual and semantic importance of words in input text, allowing for accommodation of a multi-objective optimization problem, i.e. visual and semantic importance, through diverse generation of spatial layouts without the need of manual user editing (Lao, [Col. 5, Lines 60-67 - Col. 6, Lines 1-20]).
Maheswaran further discloses:
apply, via the computer processor, the weight-based approach within a predetermined scope as defined by a knowledge graph ([Fig. 4], [0043] In such an embodiment, creating the context graph using the plurality of tokens may include applying the set of predefined sentencing rules on the context graph. In an exemplary embodiment, applying the set of predefined sentencing rules on the context graph may include applying at least one of a rule for parts of speech, a rule for punctuations, a rule for conjunctions or the like or a combination thereof [A context graph tracks to a knowledge graph. In view of the text importance vector of Lao, indicating applying a rule for parts of speech, i.e. giving nouns higher weight, indicates a weight based approach as defined by a knowledge graph, i.e. the parts of speech of Fig. 4. Wherein the predetermined scope is any input text represented through a knowledge graph, i.e. the example of Fig. 4]).
Maheswaran in view of Lao does not disclose:
apply, via the computer processor, the weight-based approach within a predetermined scope as defined by a data dictionary.
Mwarabu discloses:
apply, via the computer processor, the weight-based approach within a predetermined scope as defined by a data dictionary ([0042] In some embodiments, the supervised span classification module 225 automatically classifies spans using a dictionary data structure directed to identifying terms and phrases [Classifying spans, i.e. text segments, using a data dictionary, in view of the text importance vector of Lao, indicating classification, e.g. weighting, based on visual and/or contextual information tracks to using a data dictionary to determine the text importance vector of Lao, wherein the text importance vector represents importance of words (as would be determined through inclusion/exclusion in a data dictionary as disclosed in Mwarabu)]).
Maheswaran, Lao, and Mwarabu are considered analogous art within semantic analysis of text. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Maheswaran in view of Lao to incorporate the teachings of Mwarabu, because of the novel way to convert natural language into visual representations for classifications, improving the ability to identify relevant text spans within unstructured natural language content through more documents in a shorter amount of time (Mwarabu, [0051]).
Maheswaran in view of Lao, further in view of Mwarabu does not disclose:
receive guidelines that are mortgage industry specific; and,
wherein the data-dictionary and the knowledge graph are specific to a mortgage industry.
Kanchibhotla discloses:
receive guidelines that are mortgage industry specific ([0026] documents may have titles and subtitles, sections and sub-sections with section and sub-section headings, paragraphs and subparagraphs, domain-specific key terms and key phrases, and the like, [0053] Documents may be read sequentially from the documents repository 420 and analyzed for their content and structure, [0055] system identifies the document class 422 using a document classifier 427… a domain-specific document classifier may be developed. The domain-specific document classifier may use domain-oriented terms, [Analyzing documents for content and structure for the purposes of classification indicates that the documents are comprised of guidelines, i.e. sentences, in view of Fig. 6 of the instant app that are mortgage industry specific (see mortgage class below)]); and,
wherein the data-dictionary and the knowledge graph are specific to a mortgage domain ([0055] Similarly, for a banking domain, the classes are Mortgage, Housing and Urban Development (HUD), Truth-in-Lending (TIL), or any similar document class associated with banking [In view of the knowledge graph of Maheswaran and the data dictionary of Mwarabu, wherein Maheswaran indicates portability across domains ([0046]) indicating the mortgage domain of Kanchibhotla could be applied to the system of Maheswaran’s knowledge graph and Mwarabu’s data dictionary as Maheswaran discloses a method portable across domains a specifically discloses a sales/business domain ([0046]-[0047]) which reasonably would include mortgaging, and Mwarabu discloses sensitive data in the context of financial information ([0027]), reasonably tracking to mortgage information]).
Maheswaran, Lao, Mwarabu, and Kanchibhotla are considered analogous art within semantic analysis of text. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Maheswaran in view of Lao, further in view of Mwarabu to incorporate the teachings of Kanchibhotla, because of the novel way to identify relevant text from multiple text spans based on classifying queries dependent on domain-specific key terms and phrases (Kanchibhotla, [0026]).
Maheswaran in view of Lao, further in view of Mwarabu, further in view of Kanchibhotla does not disclose:
generate a code base corresponding to the flow chart and comprising base code snippets as individual functions based on application of the weight-based approach, wherein the code base is executable to implement the condition-action relationships.
Shyamsundar discloses:
generate a code base corresponding to the flow chart and comprising base code snippets as individual functions based on application of the weight-based approach ([0117] For example, with reference to FIG. 21, workflow designer 106 saves the workflow in storage 2104 to generate saved workflow logic containing program code that defines the workflow, [0115] The workflow steps may be selected from a workflow library (e.g., workflow library 118, as shown in FIG. 1) that stores program code defining each of the workflow steps [Each workflow step can be reasonably assumed to be representative of base code snippets as individual functions as each step has its own code, in view of the previously disclosed workflow generated through a weight-based approach]), wherein the code base is executable to implement the condition-action relationships ([0086] The first program code is configured to generate the trigger in response to receiving an email, and the second program code is configured to generate the trigger in response to retrieving an email from an inbox, [Example condition of “if an email is received, generate a trigger”]).
Maheswaran, Lao, Mwarabu, Kanchibhotla, and Shyamsundar are considered analogous art within textual/document analysis. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Maheswaran in view of Lao, further in view of Mwarabu, further in view of Kanchibhotla to incorporate the teachings of Shyamsundar, because of the novel way to develop workflows for any number of available services in business and/or consumer settings to receive information including any number of conditions to perform any number of actions based on the received information and conditions without the need of an experienced software developer to generate code/inputs for performance of these actions (Shyamsundar, [0002]-[0005]).
Maheswaran, Lao, Mwarabu, Kanchibhotla, and Shyamsundar do not disclose:
generate a flow chart based on the weight-based approach and the hierarchical arrangement of the node-tree structure, wherein the flow chart represents a transformation of the plurality of text segments into illustrative representations and captures a flow of all required condition-action relationships identified from the hierarchical node-tree structure (emphasis added to underlined portions).
Danielyan discloses generation of a semantic graph with regard to input ([0035]), wherein constituents of input text may also be represented as a tree ([0099]), wherein the constituents of each of these representations can be ordered in a semantic hierarchy 1010 (see [Fig. 10]) based on style of text ([0092]). Danielyan does not disclose generation of flow charts based on the generated hierarchies of the syntax tree(s). The components of the semantic hierarchy are merely for purposes of expressing semantic relationships between components ([0087]), not required condition-action relationships.
Ziuzin discloses named entity recognition for the purposes of resolving references between natural text tokens as part of a workflow ([0030]), wherein Ziuzin discloses semantic trees ([0060]) and information graphs representing semantic structure ([0067], [0104]). Consider the semantic hierarchy disclosed ([Fig. 8, 810]). Similarly to Danielyan, Ziuzin does not disclose generation of flow charts based on the generated hierarchies of the syntax tree(s). The components of the semantic hierarchy are merely for purposes of expressing semantic relationships between components as previously cited, not required condition-action relationships.
Park discloses structuring keywords in a tree form using a domain knowledge base including a hierarchical structure among the entity information of the keywords ([0052]). Further, Park discloses a weight value module which adds weights to entity information and nodes of the keyword tree which considers font size, thickness, or font type ([0080]). This tracks to the “node-tree structure that represents a weight for each of the plurality of text segments, wherein the node-tree structure organizes…in a hierarchical arrangement based on the depth-parameter numbers”, wherein the depth-parameters numbers track to the weight values. Further, Park discloses a semantic mapper module for mapping semantic path information to keyword information ([0094]). Even if the “path information” could be considered to be a work flow/flow chart, it is not displaying the appropriate information required by the claims, i.e. condition-action relationships. Similar to Danielyan and Ziuzin, the purposes of the representations of Park are for determining semantic relations between components of input text, not for purposes of determining actions to be performed.
The examiner believes the cited art to be the best available art before the effective filing date of the claimed invention, 04/27/2023. As can be seen with the cited art, there is nothing which explicitly or implicitly discloses the concept of flow chart generation representing condition-action pairs to be performed, wherein the flow chart is generated based on a weight-based approach which considers visual appearance/format/structure of text to create a hierarchical node-tree structure from which the condition-action relationships are gathered and transformed into flow chart operations to be transformed into code. Sources like Danielyan, Ziuzin, and Park might disclose representations, i.e. graphs/tree/maps, of input text similar to those claimed, but they are not being used for the same purposes of identification of condition-action relationships. The cited art is focused on gathering semantic relationship information between the words without the additional step of forming actions based on a “condition” being met in the structure of the input texts in the form of a flow chart. These pieces of art merely function to improve representations of texts/documents for purposes of more accurate and/or improved information consolidation and/or response generation; therefore, claim 1 has been deemed to be containing allowable subject matter. Claim 11 recites identical subject matter in the form of a method. Nothing is introduced in claim 11 which is patentably distinct from claim 1; therefore, claim 11 has also been deemed to be containing allowable subject matter. All claims dependent upon allowable base claims are also allowable; therefore, claims 2, 4-10, 12, 14-20 are also allowed.
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Conclusion
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/THEODORE WITHEY/Examiner, Art Unit 2655
/ANDREW C FLANDERS/Supervisory Patent Examiner, Art Unit 2655