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 . Claims 1-15 are presented in the case.
Information Disclosure Statement
The information disclosure statements submitted on 05/18/2022 and 01/23/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Priority
Applicant's claim for the benefit of a provisional application 62/940476 filed on 11/26/2019 is acknowledged.
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- 15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”)
Claim 1 and 9 have the following abstract idea analysis.
Step 1: The claim is directed to “a method and system”. The claims are directed to the statutory categories accordingly.
Step 2A Prong 1: claims recite the abstract idea limitations of "obtain requirement information, the requirement information defining a requirement;" and "identify sub-parts of the requirement based on words of the requirement;”. These limitations include mental concepts (recognizing or writing parts of words). Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III). The specification also provides example operations performed such as identifying subject, noun or verbs. USPGPUB ¶35. Other sections of the claims such as "generate a requirement graph model of the requirement, the requirement graph model including nodes for the sub-parts of the requirement;", "connect the requirement graph model to a constraint graph model based on one or more of the nodes of the requirement graph model, wherein connection of the requirement graph model to the constraint graph model forms a combined graph model; and", and "perform analysis based on the combined graph model" are advanced processes, too generic or high level to be listed as a mental or mathematical concept given the available descriptions and MPEP comparisons.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. Merely invoking "AI algorithm operation accelerator", "a register" or "memory unit" does not yield eligibility. Claims are still in line with math concepts such as claim 1, 11, 21 and 23 are not specific to a practical application. The additional elements as such are processors and instructions which do not include specialized hardware. See MPEP § 2106.05(f).
Claim 1 and 9 do not include a particular field but even doing so may not be sufficient to overcome the abstract idea rejection. Merely applying an model to a field or data without an advancement in the new field or new hardware is ineligible. MPEP § 2106.05(h).
Step 2B: The claims do not contain significantly more than their judicial exceptions. Processors and other hardware are in their standard forms in the field. These additional elements are well-understood, routine, and conventional activity, see MPEP 2106.05(d)(II). Claims lacks any particular "how" or algorithm for a solution in a field in a novel way. Claims require more specificity on processes that would be incapable of simple mathematics, mental processes or use more substantial structure than conventional devices such as non-textbook implementations.
Regarding claims 2-8 and 10-15, merely narrow the previously recited abstract idea limitations with more abstract concepts and/or routine fundamental processes. For the reasons described above with respect to claim 1 and 9 this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Abstract idea steps 1, 2A prong 1 and 2 remain the same as independent analysis above. See specification for more practical application concepts as none are seen in claims 2-8 and 10-15.
With respect to step 2B These claims disclose similar limitations described for the independent claims above and do not provide anything significantly more than mathematical or mental concepts. Claims 2-8 and 10-15 recite the additional elements of "wherein the words of the requirement are stored in a prose format, and identification of the sub-parts of the requirement includes decomposition of the words of the requirement in the prose format into the sub-parts. wherein the sub-parts of the requirement includes a subject, an object, a predicate, and a context, and the requirement graph model includes a subject node corresponding to the subject of the requirement, an object node corresponding to the object of the requirement, a predicate node corresponding to the predicate of the requirement, and a context node corresponding to the context of the requirement. wherein the constraint graph model includes a control structure node, an unsafe condition node, and a hazard node. wherein the constraint graph model further includes a constraint requirement graph model. wherein the requirement graph model is connected to the constraint graph model based on matching between one or more nodes of the requirement graph model with one or more nodes of the constraint requirement graph model. wherein the requirement is analyzed to determine a grading that reflects a quality of the requirement. wherein the requirement graph model is dynamically generated based on need." These elements are more abstract concepts, generic applications to a field of use or well-understood, routine, conventional activity (see MPEP § 2106.05(d) and can't be simply appended to qualify as significantly more or being a practical application. What type of application, or structure of components beyond generic machine learning is still unknown for these claims. Therefore claims 2-10, 12-20 and 22. also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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 ,7, 9-10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Narendra et al. (US 8504506 B2 hereinafter Narendra) in view of Kim et al. (US 20170132203 A1 hereinafter Kim)
As to independent claim 1, Narendra teaches a system for analyzing requirements, the system comprising: [analysis of requirements system Col.3 ln. 15-16]
one or more physical processors configured by machine-readable instructions to: [processor and instructions Col. 8 ln 49-63]
obtain requirement information, the requirement information defining a requirement; [repository of requirement Col.3 ln. 15-16 "an asset repository provides that a user may search the repository based upon project requirements and perform a fit gap analysis on those assets that appear to match the requirements"]
connect the requirement graph model to a constraint graph model based on one or more of the nodes of the requirement graph model, wherein connection of the requirement graph model to the constraint graph model forms a combined graph model; and [connects models of requirements Fig. 2 201E and constraints Fig. 2 201D which are linked with traceability Col. 4-5 ln. 53-6 " the ARCM model generator 201F from the generic asset requirement modeler 201E and solution constraints modeler 201D outputs. The initial ARCM model 201G is specific to the solution requirement model 201A and will have active traceability links and may be stored in the repository 204"]
perform analysis based on the combined graph model. [uses model to perform fit gap analysis Col. 5-6 ln. 49-10 " iterative fit gap analysis 203E may involve matching and ranking the relevance of a given ACAM model with the ARCM model"]
Narendra does not specifically teach identify sub-parts of the requirement based on words of the requirement and generate a requirement graph model of the requirement, the requirement graph model including nodes for the sub-parts of the requirement.
However, Kim teaches identify sub-parts of the requirement based on words of the requirement; [Takes requirements (RFP) and performs NLP to create subparts (subject, verb, noun) ¶21 " a requirement 252 may be mapped to one or more sentences 228 in a document 214. The sentence 228 is parsed into subject 234, verb 232, and noun phrase 230"]
generate a requirement graph model of the requirement, the requirement graph model including nodes for the sub-parts of the requirement; [uses the text sections to build a model ¶24-25 "all sections and text that are cross-referenced are linked together to build a holistic, connected model of all documents in the RFP package"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the requirement analysis disclosed by Narendra by incorporating the identify sub-parts of the requirement based on words of the requirement and generate a requirement graph model of the requirement, the requirement graph model including nodes for the sub-parts of the requirement disclosed by Kim because both techniques address the same field of document analysis and by incorporating Kim into Narendra automates manual text analysis enabling better understanding of a large set of documents [Kim ¶2-3]
As to dependent claim 2, the rejection of claim 1 is incorporated, Narendra and Kim further teach wherein the words of the requirement are stored in a prose format, and identification of the sub-parts of the requirement includes decomposition of the words of the requirement in the prose format into the sub-parts. [Kim subject, verb, noun with sentences is a prose format in words ¶21]
As to dependent claim 7, the rejection of claim 1 is incorporated, Narendra and Kim further teach wherein the requirement is analyzed to determine a grading that reflects a quality of the requirement. [Kim confidence scores on requirements (grade) ¶26 "compute confidence score for each requirement based on the assessment of the values of features "]
As to independent claim 9, Narendra teaches a method for analyzing requirements, the method comprising: [analysis of requirements system Col.3 ln. 15-16]
obtaining requirement information, the requirement information defining a requirement; [repository of requirement Col.3 ln. 15-16 "an asset repository provides that a user may search the repository based upon project requirements and perform a fit gap analysis on those assets that appear to match the requirements"]
connecting the requirement graph model to a constraint graph model based on one or more of the nodes of the requirement graph model, wherein connection of the requirement graph model to the constraint graph model forms a combined graph model; and [connects models of requirements Fig. 2 201E and constraints Fig. 2 201D which are linked with traceability Col. 4-5 ln. 53-6 " the ARCM model generator 201F from the generic asset requirement modeler 201E and solution constraints modeler 201D outputs. The initial ARCM model 201G is specific to the solution requirement model 201A and will have active traceability links and may be stored in the repository 204"]
performing analysis based on the combined graph model. [uses model to perform fit gap analysis Col. 5-6 ln. 49-10 " iterative fit gap analysis 203E may involve matching and ranking the relevance of a given ACAM model with the ARCM model"]
Narendra does not specifically teach identifying sub-parts of the requirement based on words of the requirement and generating a requirement graph model of the requirement, the requirement graph model including nodes for the sub-parts of the requirement.
However, Kim teaches identifying sub-parts of the requirement based on words of the requirement; [Takes requirements (RFP) and performs NLP to create subparts (subject, verb, noun) ¶21 " a requirement 252 may be mapped to one or more sentences 228 in a document 214. The sentence 228 is parsed into subject 234, verb 232, and noun phrase 230"]
generating a requirement graph model of the requirement, the requirement graph model including nodes for the sub-parts of the requirement; [uses the text sections to build a model ¶24-25 "all sections and text that are cross-referenced are linked together to build a holistic, connected model of all documents in the RFP package"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the requirement analysis disclosed by Narendra by incorporating the identifying sub-parts of the requirement based on words of the requirement and generating a requirement graph model of the requirement, the requirement graph model including nodes for the sub-parts of the requirement disclosed by Kim because both techniques address the same field of document analysis and by incorporating Kim into Narendra automates manual text analysis enabling better understanding of a large set of documents [Kim ¶2-3]
As to dependent claim 10, the rejection of claim 9 is incorporated, Narendra and Kim further teach wherein the words of the requirement are stored in a prose format, and identification of the sub-parts of the requirement includes decomposition of the words of the requirement in the prose format into the sub-parts. [Kim subject, verb, noun with sentences is a prose format in words ¶21]
As to dependent claim 15, the rejection of claim 9 is incorporated, Narendra and Kim further teach wherein the requirement is analyzed to determine a grading that reflects a quality of the requirement. [Kim confidence scores on requirements (grade) ¶26 "compute confidence score for each requirement based on the assessment of the values of features "]
Claims 3 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Narendra and Kim as applied in the rejection of claim 2 and 10 above, and further in view of Cui et al. (US 11151992 B2 hereinafter Cui)
As to dependent claim 3, Narendra and Kim teach the method of claim 2 above that is incorporated,
Narendra and Kim further teach wherein the sub-parts of the requirement includes a subject, an object, a predicate, and a context, and [Kim predicate ¶50 subject, verb, object ¶29.]
Narendra and Kim do not specifically teach the requirement graph model includes a subject node corresponding to the subject of the requirement, an object node corresponding to the object of the requirement, a predicate node corresponding to the predicate of the requirement, and a context node corresponding to the context of the requirement.
However, Cui teaches the requirement graph model includes a subject node corresponding to the subject of the requirement, an object node corresponding to the object of the requirement, a predicate node corresponding to the predicate of the requirement, and a context node corresponding to the context of the requirement. [Fig. 5B 523-527 illustrates subject, object and predicate nodes Col. 19 ln. 14-33 "object node 523, “apples” node 524, predicate node 525, “love” node 526, subject node 527"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the modeling disclosed by Narendra and Kim by incorporating the requirement graph model includes a subject node corresponding to the subject of the requirement, an object node corresponding to the object of the requirement, a predicate node corresponding to the predicate of the requirement, and a context node corresponding to the context of the requirement disclosed by Cui because all techniques address the same field of machine learning and by incorporating Cui into Narendra and Kim enhances interactivity of AI services for a more contextual aware problem solving [Cui Col. 21 ln. 23-47]
As to dependent claim 11, Narendra and Kim teach the method of claim 10 above that is incorporated,
Narendra and Kim further teach wherein the sub-parts of the requirement includes a subject, an object, a predicate, and a context, and [Kim predicate ¶50 subject, verb, object ¶29.]
Narendra and Kim do not specifically teach the requirement graph model includes a subject node corresponding to the subject of the requirement, an object node corresponding to the object of the requirement, a predicate node corresponding to the predicate of the requirement, and a context node corresponding to the context of the requirement.
However, Cui teaches the requirement graph model includes a subject node corresponding to the subject of the requirement, an object node corresponding to the object of the requirement, a predicate node corresponding to the predicate of the requirement, and a context node corresponding to the context of the requirement. [Fig. 5B 523-527 illustrates subject, object and predicate nodes Col. 19 ln. 14-33 "object node 523, “apples” node 524, predicate node 525, “love” node 526, subject node 527"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the modeling disclosed by Narendra and Kim by incorporating the requirement graph model includes a subject node corresponding to the subject of the requirement, an object node corresponding to the object of the requirement, a predicate node corresponding to the predicate of the requirement, and a context node corresponding to the context of the requirement disclosed by Cui because all techniques address the same field of machine learning and by incorporating Cui into Narendra and Kim enhances interactivity of AI services for a more contextual aware problem solving [Cui Col. 21 ln. 23-47]
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Narendra Kim and Cui as applied in the rejection of claim 3 and 11 above, and further in view of Murez et al. (US 7716239 B2 hereinafter Murez)
As to dependent claim 4, Narendra, Kim and Cui teach the method of claim 3 above that is incorporated,
Narendra, Kim and Cui do not specifically teach wherein the constraint graph model includes a control structure node, an unsafe condition node, and a hazard node.
However, Murez teaches wherein the constraint graph model includes a control structure node, an unsafe condition node, and a hazard node. [Hazard analysis with safeguards and nodes Col. 5 ln. 4-42 "instruments, alarms, safeguards, operating limits, and other process information are also populated into the node template for the particular node 13 from the master lists 34"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the modeling disclosed by Narendra, Kim and Cui by incorporating the wherein the constraint graph model includes a control structure node, an unsafe condition node, and a hazard node disclosed by Murez because all techniques address the same field of data analysis and by incorporating Murez into Narendra, Kim and Cui improve the data analysis of graphs with filtering a user input [Murez Col. 3 ln. 11-35]
As to dependent claim 5, the rejection of claim 4 is incorporated, Narendra, Kim, Cui and Murez further teach wherein the constraint graph model further includes a constraint requirement graph model. [Narendra constraint with requirements Col. 4 ln. 60-65 "constraints modeler 201D and requirements through the asset requirement modeler 201E"]
As to dependent claim 6, the rejection of claim 5 is incorporated, Narendra, Kim, Cui and Murez further teach wherein the requirement graph model is connected to the constraint graph model based on matching between one or more nodes of the requirement graph model with one or more nodes of the constraint requirement graph model. [Cui share related nodes with links (matching) Col. 18 ln. 14-23 "related nodes are associated and/or shared. For example, a node corresponding to a person's name from an input sentence is linked to a node associated with a second sentence that utilizes a pronoun (e.g., “I”) in place of the same person's name."]
As to dependent claim 12, Narendra, Kim and Cui teach the method of claim 11 above that is incorporated,
Narendra, Kim and Cui do not specifically teach wherein the constraint graph model includes a control structure node, an unsafe condition node, and a hazard node.
However, Murez teaches wherein the constraint graph model includes a control structure node, an unsafe condition node, and a hazard node. [Hazard analysis with safeguards and nodes Col. 5 ln. 4-42 "instruments, alarms, safeguards, operating limits, and other process information are also populated into the node template for the particular node 13 from the master lists 34"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the modeling disclosed by Narendra, Kim and Cui by incorporating the wherein the constraint graph model includes a control structure node, an unsafe condition node, and a hazard node disclosed by Murez because all techniques address the same field of data analysis and by incorporating Murez into Narendra, Kim and Cui improve the data analysis of graphs with filtering a user input [Murez Col. 3 ln. 11-35]
As to dependent claim 13, the rejection of claim 12 is incorporated, Narendra, Kim, Cui and Murez further teach wherein the constraint graph model further includes a constraint requirement graph model. [Narendra constraint with requirements Col. 4 ln. 60-65 "constraints modeler 201D and requirements through the asset requirement modeler 201E"]
As to dependent claim 14, the rejection of claim 13 is incorporated, Narendra, Kim, Cui and Murez further teach wherein the requirement graph model is connected to the constraint graph model based on matching between one or more nodes of the requirement graph model with one or more nodes of the constraint requirement graph model. [Cui share related nodes with links (matching) Col. 18 ln. 14-23 "related nodes are associated and/or shared. For example, a node corresponding to a person's name from an input sentence is linked to a node associated with a second sentence that utilizes a pronoun (e.g., “I”) in place of the same person's name."]
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Narendra Kim and Cui as applied in the rejection of claim 1 above, and further in view of Berenbach et al. (US 20130060546 A1 hereinafter Berenbach)
As to dependent claim 8, Narendra and Kim teach the method of claim 1 above that is incorporated,
Narendra and Kim do not specifically teach wherein the requirement graph model is dynamically generated based on need.
However, Berenbach teaches wherein the requirement graph model is dynamically generated based on need. [Fig. 2 illustrates user based diagram or graph based on user need (suits intended viewer) ¶64 ". In generating the diagram, a particular view may be adopted to suit the intended viewer. The view may then be displayed for the intended viewer (Step S206)"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the modeling disclosed by Narendra and Kim by incorporating the wherein the requirement graph model is dynamically generated based on need disclosed by Berenbach because all techniques address the same field of requirement analysis and by incorporating Berenbach into Narendra and Kim provides a more complete view of all the various factors and objectives that are considered in design of models [Berenbach ¶6-7]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Manolios (US 9639450 B2) teaches creating a unified graph with requirements as nodes (see Col. 16 ln. 41-62)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Beau Spratt whose telephone number is 571 272 9919. The examiner can normally be reached 8:30am to 5:00pm (PST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch can be reached at 571 272 7212. The fax phone number for the organization where this application or proceeding is assigned is 571 483 7388.
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143