DETAILED ACTION
Introduction
This office action is in response to applicant’s request for continued Examination filed 10/9/2025. Applicant’s IDS have been considered. The claim to foreign priority is acknowledged. The Examiner notes, any Double Patenting rejections will be held in abeyance pending applicant’s response and amendments, as multiple applications by the applicant are currently being considered by the Examiner.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 submissions filed on 6/25/2025, 8/12/2024, 11/4/2025 have been entered.
Response to Arguments
Applicant’s arguments, see remarks, filed 10/9/2025, with respect to the rejection(s) of claim(s) 1, 6-10, 22, 23 and 28-30 under 35 USC 103 have been fully considered and are not fully persuasive.
More specifically, applicant argues,
“Claim 1 is not obvious over Tunstall-Pedoe in view of Mabbu, Burceanu and Lin, because these citations fail to disclose or to make obvious the Claim 1 limitation that the machine readable processable language into which the customer reviews of products or services are translated is the same as the language in which the reasoning passages are represented. Therefore because the citations do not include each element claimed, namely the portions of Claim 1 identified in the previous sentence, Claim 1 is not obvious over Tunstall-Pedoe in view of Mabbu, Burceanu and Lin:MPEP §2143(A).
On pages 11 to 12 of the Office Action, the Office cites Burceanu against the Claim 1 limitations which recite that a machine learning system automatically translates [customer reviews of products or services] into a machine readable processable language. On page 13 of the Office Action, the Office cites Lin against the Claim 1 limitations which recite reasoning passages represented in the machine-readable processable language.
But Burceanu provides no suggestion that the language into which translation occurs is one in which reasoning passages may be represented, as required by Claim 1. Regarding the use of the word "reason" in Burceanu, Burceanu includes:
"The architecture and/or parameter values of NL encoder 70b may differ
substantially from that of AL encoder 70a. One reason for such difference is that
the vocabularies of artificial and natural languages typically differ from each
other, so that input arrays representing NL sentences may differ at least in size
from input arrays representing AL sentences. Another reason why encoders 70a-b may have distinct architectures is that the grammar/syntax of artificial languages typically differs substantially from that of natural languages." Burceanu, para. [0053].”
But this does not teach or suggest that the language into which translation occurs is one in which reasoning passages may be represented.
And Lin provides no suggestion that the language in which reasoning passages may be represented is one into which translation of customer reviews of products or services occurs, as required by Claim 1.”
“With regard to the Office's comment that "The applicant provides absolutely no definition or specification of what the machine-readable language is",
the Applicant notes that earlier in Claim 1 it is recited "a machine learning system automatically translating customer reviews of products or services into a machine readable processable language", and therefore the machine-readable language needs to be one into which the customer reviews of products or services can be translated. This does provide some definition of what the machine-readable language is.”
Examiner’s Response.
The applicant above states, “the Applicant notes that earlier in Claim 1 it is recited "a machine learning system automatically translating customer reviews of products or services into a machine-readable processable language", and therefore the machine-readable language needs to be one into which the customer reviews of products or services can be translated. This does provide some definition of what the machine-readable language is.”
The Examiner notes this does not provide any specific teaching as to what the machine-readable processable language is. It simply states that the customer review is translated into this language, and that the passages are represented in the machine-readable processable language.
Therefore, the Examiner notes, the current rejection is based on a combination of references. Wherein, as the applicant provides absolutely zero specific citations anywhere in the original disclosure of what the “the machine-readable language” is and thus, does not claim any specific “machine-readable language. It is then noted that, Tunstall teaches translating information into a machine-readable processable language. Burceanu further teaches using a machine-learning system to automatically translating the information into a machine-readable processable language. Burceanu is not required to explicitly state that the machine-readable language, must also represent reasoning passages. The Examiner has not relied upon Burceanu for this feature, see the rejection below. It is the Examiner’s position that, Lin, which absolutely, and explicitly teaches of reasoning steps, which in Fig. 11, which his system, uses, also incorporates a machine-readable processable language (although the exact machine-readable language is also not described).
The Examiner notes, having a “common machine-readable language” for analysis, and processing data is not novel, and well known in the art for a plurality of reasons.
“Converting all data into a common machine-readable language is essential for several reasons:
Enhanced Efficiency: Machine-readable data allows for quick and efficient extraction of information, which is crucial for data analysis and decision-making.
Improved Accuracy: Machine-readable formats ensure that data is processed accurately without human error, which is vital for maintaining the integrity of the data.
Standardization: Machine-readable data is standardized, making it easier to share, access, and analyze across different systems and platforms.
Future-Proofing: By converting data into machine-readable formats, organizations can ensure that their data is ready for future technological advancements and data-driven applications.
Transparency and Accountability: Machine-readable data promotes transparency and accountability, as it can be easily verified and updated.”
Therefore, in order for the system to analyze data, the system should have the analysis steps, in a machine-readable language, in order for the system to understand and execute the analysis or reasoning steps. Furthermore, the data to be analyzed, should have been converted into that machine-readable language. The use of a machine learning system to automatically convert data into a machine-readable language is motivated by the learning, training, and automated benefits of said translating. All of these should be in a common machine-readable language. Therefore, even if the applicant argument that the exact “machine-readable processable” languages are not the same. Wherein, Tunstall teaches a conversion into a machine-readable processable language, Burceanu teaches also, of a conversion, and also teaches of explicit types of machine-readable languages for conversion, and Lin teaches reasoning steps, executed by the system. It would be obvious to one of ordinary skill in the art to make sure that that entire system, comprises a common, machine-readable processable language, in order for data analysis, efficiency, and accurately processing the data using a common format.
In summary, converting all data into a common machine-readable language is a strategic move that enhances the efficiency, accuracy, and accessibility of data.
In the event that the processable language is Universal, then all machine-readable processable languages could be interpreted to be within the universal language, and thus a same language. However, for purposes of examination and expediting prosecution, the Examiner has modified the reject below to indicate that data into a common, “same” machine-readable processable language, is known in the art, with respect to reasoning passages and user input data, such as a query or statement (which by combination, includes customer reviews), and is motivated by reasons listed above and below, and thus cites Byrnes et al. (Byrnes, US 2020/0193286) which explicitly teaches a set of reasoning rules being converted into a formal language understandable by a computing device, and also having user input data, converted into the same formal language, thus the common/same machine-readable language is used in the system for achieve a response to a query, including explanations.
Applicant’s remaining arguments are based on the above arguments and are deemed non-persuasive in light of the above response and rejections as seen below.
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 (i.e., changing from AIA to pre-AIA ) 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, 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, 6-10, 22, 23, and 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Tunstall-Pedoe (US 2013/0254182) in view of Mabbu (A Semantic Knowledge Engine Using Automated Knowledge Extraction from World Wide Web), in view of Burceanu et al. (Burceanu, US 2020/0004831) in view of Lin et al. (Lin, US 2021/0279621), and further in view of Byrnes et al. (Byrnes, US 2020/0193286).
As per claim 1, Tunstall-Pedoe teaches a computer implemented method for the automated analysis or use of data, comprising the steps of:
(a) [a machine learning system automatically] translating [customer reviews of products or services] into a machine readable processable language, wherein the machine learning system generates respective semantic nodes and passages that represent respective customer reviews of products or services (ibid-Tunstall-Pedoe, paragraphs [0024-0027, 0261-0268, 0442-0463]-see his words or sequence of words in the natural language query, and automatic query/answering and above semantic nodes/passages discussion, with respect to the knowledge base representation data, wherein from a user, a translation into a machine readable processable language, paragraphs [0122-0151, 0026, 0086-0092]-his nodes, and links, all as objects in machine readable form knowledge representation);
(b) storing in a non-transitory storage medium a structured, machine-readable representation of data that conforms to the machine-readable processable language generated in step (a) (paragraphs [0120, 0122-0151, 0023, 0026, 0086-0092]-his nodes, and links, all as objects in machine readable form knowledge representation, in storage);
[wherein the structured, machine-readable representation of data relates to the customer reviews of products or services];
(c) automatically processing the structured, machine-readable representation of data stored in step (b) (ibid-see his automatic analysis of input data, see also paragraphs [0245, 0739]) [and processing reasoning passages represented in the machine-readable processable language to represent semantics of reasoning steps, to reason, to analyse the customer reviews];
in which the structured, machine-readable representation of data that conforms to the machine-readable language comprises semantic nodes and passages (ibid-Tunstall-Pedoe paragraphs [0122-0151, 0026, 0086-0092]-his nodes, and links, all as objects in machine readable form knowledge representation); and in which each semantic node represents a respective entity and is represented by a respective identifier (ibid-Tunstall-Pedoe paragraphs [0122-0151, 0026, 0086-0092]-his nodes, and links, all as objects in machine readable form knowledge representation, and corresponding identifiers for the objects/entities); and each passage is either (i) a semantic node or (ii) a combination of semantic nodes (ibid-see above translating discussion, corresponding semantic graphs, and paragraphs [0024, 0174]-the generated machine-readable representations providing meaning for the word or sequence of words); and where machine-readable meaning comes from choice of semantic nodes and their combination and ordering as passages (see his semantic graph, corresponding nodes and his sequence or combination of nodes as his passages, each node/link as an object comprising entities, “any entity” that can possibly be denoted in a natural language, his choice of combination and ordering of nodes providing meaning).
Tunstall-Pedoe lacks explicitly teaching that which Mabbu teaches translating customer reviews of products or services;
wherein the [the structure, machine-readable representation of] data relates to the customer reviews of products or services (pages 22, 26, 6-9 his Reviews, and corresponding machine-readable facts in a database, based on the reviews, his Yelp reviews, page 6-his knowledge base and semantic search engine, based on facts, as applied to his analysis, via crawl of and conversion of reviews, using the semantic search engine, see his Evi technology knowledge representation discussion, thus hereinafter the NL data and corresponding translation data, and machine-readable representation data of Tunstall-Pedoe, as the customer review data, based on the combination below).
(c) automatically processing the structured, machine-readable representation of data stored in step (b) to analyse the customer reviews (ibid-his analysis thereof, based on his semantic answering of questions based on the data input/reviews).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Tunstall-Pedoe and Mabbu to combine the prior art element of translating natural language into a machine-readable representation form as taught by Tunstall-Pedoe with having the data, as the natural language, being customer reviews of a product or service, as taught by Mabbu, as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be an automatic translation from a natural language data, based on reviews, to the machine-readable representation, in order to answer questions about reviews (ibid-Tunstall-Pedoe, question answering, and Mabbu-his question answering based on Web data including reviews).
Tunstall-Pedoe with Mabbu lack explicitly teaching that which Burceanu teaches (a) a machine learning system automatically translating [customer reviews of products or services] into a machine readable language, wherein the machine learning system generates respective semantic nodes and passages that represent respective customer reviews of products or services (Burceanu, paragraphs [0033-0044, 0029]-his machine learning, RNN, LSTM and neural network for generating semantic nodes and passages via an output array, which represent the words or sequence of words in a natural language).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Tunstall-Pedoe and Mabbu and Burceanu to combine the prior art element of translating natural language into a machine-readable representation form as taught by Tunstall-Pedoe with customer reviews as data to be analyzed as taught by Mabbu with using a neural network for learning to generate the machine-readable representations as taught by Burceanu as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be an automatic translation from a natural language to the machine-readable representation, wherein the translation model is trained using a neural network (ibid-Burceanu, see also abstract).
The above combination lacks explicitly teaching that which Lin teaches, processing reasoning passages represented in the machine-readable processable language to represent semantics of reasoning steps, to reason (paragraphs [0081, 0003, 0021], Figs. 11, 12-as his reasoning passages represented in a machine-readable processable language to represent semantics of “reasoning steps”, to reason, as applied to analysis or use of data).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Tunstall-Pedoe and Mabbu and Burceanu and Mabbu and Lin to combine the prior art element of translating natural language into a machine-readable representation form as taught by Tunstall-Pedoe with customer reviews as data to be analyzed as taught by Mabbu with using a neural network for learning to generate the machine-readable representations as taught by Burceanu with a reasoning agent as taught by Lin as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be an automatic translation from of data, such as the customer reviews in a natural language to the machine-readable representation, wherein the translation model is trained using a neural network, wherein the reasoning and explainability with respect to an output of processed/analyzed data is realized and presented to a user (ibid-Burceanu, see also abstract, ibid Lin, see also Figs. 10-12, paragraph [0081]), wherein Converting all data into a common machine-readable language provides a predictable result including, Enhanced Efficiency: Machine-readable data allows for quick and efficient extraction of information, which is crucial for data analysis and decision-making. Improved Accuracy: Machine-readable formats ensure that data is processed accurately without human error, which is vital for maintaining the integrity of the data. Standardization: Machine-readable data is standardized, making it easier to share, access, and analyze across different systems and platforms. Future-Proofing: By converting data into machine-readable formats, organizations can ensure that their data is ready for future technological advancements and data-driven applications. Transparency and Accountability: Machine-readable data promotes transparency and accountability, as it can be easily verified and updated.
The above combination lacks explicitly teaching that which Byrnes teaches, (a) [a machine learning system automatically] translating [customer reviews of products or services] into a machine readable processable language, and processing reasoning passages represented in the machine-readable processable language to represent semantics of reasoning steps, to reason, to analyse [the customer reviews] (paragraphs [0024, 0029, 0044, 0048], Fig. 1, his user input/data, and corresponding reasoning as encoded, into a specific formal language understandable by the computing device, wherein the reasoning and user query/data are mapped into the formal language understandable by the machine, thus the machine-readable processable language).
Thus, it would have been obvious to one of ordinary skill in the linguistics art, before the effective filing date of the invention, as all the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods (computer implemented techniques and algorithms combining processes and steps in natural language processing), in view of the teachings of Tunstall-Pedoe and Mabbu and Burceanu and Lin and Byrnes to combine the prior art element of translating natural language into a machine-readable representation form as taught by Tunstall-Pedoe with customer reviews as data to be analyzed as taught by Mabbu with using a neural network for learning to generate machine-readable representations as taught by Burceanu with a reasoning agent as taught by Lin with having the machine-readable language being the same, as taught by Byrnes, as each element performs the same function as it does separately, as the combination would yield predictable results, KSR International Co. v. Teleflex Inc., 550 US. -- 82 USPQ2nd 1385 (2007), wherein the predictable result would be an automatic translation from of data, such as the customer reviews in a natural language to the machine-readable representation, wherein the translation model is trained using a neural network, wherein the reasoning and explainability with respect to an output of processed/analyzed data is realized and presented to a user (ibid-Burceanu, see also abstract, ibid Lin, see also Figs. 10-12, paragraph [0081]), wherein Converting all data into a common machine-readable language provides a predictable result including, Enhanced Efficiency: Machine-readable data allows for quick and efficient extraction of information, which is crucial for data analysis and decision-making. Improved Accuracy: Machine-readable formats ensure that data is processed accurately without human error, which is vital for maintaining the integrity of the data. Standardization: Machine-readable data is standardized, making it easier to share, access, and analyze across different systems and platforms. Future-Proofing: By converting data into machine-readable formats, organizations can ensure that their data is ready for future technological advancements and data-driven applications. Transparency and Accountability: Machine-readable data promotes transparency and accountability, as it can be easily verified and updated (see Byrness, paragraphs [0024, 0044, 0046, 0063, 0093, 0099], and abstract).
As per claim 6, Tunstall-Pedoe with Mabbu with Burceanu with Lin with Byrnes further make obvious the method of Claim 1 including the step of automatically translating customer reviews of products or services expressed in a natural language into the machine- readable processable language, and in which a structure of a sequence of words is compared with known machine-readable processable language structures in the non-transitory storage medium to identify similarities (Tunstall-Pedoe, ibid-paragraphs [0077, 0530-0532]-his sequence of assertions and facts, for NL and corresponding structures in machine-readable language as stored, are compared, with respect to the NL data as customer reviews as disclosed above).
As per claim 7, Tunstall-Pedoe with Mabbu further with Burceanu with Lin with Byrnes make obvious the method of Claim 1 including the step of automatically translating the customer reviews of products or services into the machine-readable processable language by referencing a store of previously identified correct translations between a natural language and the machine-readable processable language (Tunstall-Pedoe, paragraphs [0385-0390, 0120, 0240-0245], Figs. 6, 9-11-his cache of truths for previous correct translations of a query, and quick retrieval thereof).
As per claim 8, Tunstall-Pedoe with Mabbu with Burceanu with Lin with Byrnes further make obvious the method of Claim 1 including the step of automatically translating the customer reviews of products or services into the machine-readable processable language by utilising a pipeline of functions which transform a sequence of words into a series of intermediate forms (Figs. 9-11, as including his pipeline of functions for translation of words to machine-readable knowledge representation, as with respect to the customer reviews as discussed in claim 1).
As per claim 9, Tunstall-Pedoe with Mabbu with Burceanu with Lin with Byrnes make obvious the method of Claim 1 in which the machine learning system is a neural network system, such as a deep learning system (ibid-Burceanu, see claim 3, RNN/LSTM learning network, paragraph [0045]).
As per claim 10, Tunstall-Pedoe with Mabbu with Burceanu with Lin with Byrnes make obvious the method of Claim 1 in which the machine learning system has been trained on training data comprising natural language and a corresponding structured machine- readable representation (ibid-Burceanu, see also paragraph [0037-0039]-his AL training corpus, NL-training corpora, Fig. 5 and previous machine learning, neural network discussion, see also Tunstall-Pedoe, knowledge representation database as AL corpus).
As per claim 22, Tunstall-Pedoe with Mabbu with Burceanu with Lin with Byrnes make obvious the method of Claim 1 which includes the step of learning new information and representing the new information in the structured, machine-readable representation of data that conforms to the machine-readable processable language (ibid-Tunstall-Pedoe, ibid-see above, translate, paragraphs [0077, 0082-0084]-new facts, relationships, question answering, reasoning/inference, processing of natural language content, paragraphs [0024-0026,0093, 0533], Figs. 41-42-see his question answer, and natural language response discussion, learning and new information incorporation into the knowledge base).
As per claim 23, Tunstall-Pedoe with Mabbu with Burceanu with Lin with Byrnes make obvious the method of Claim 1 which includes the step of (i) receiving or sequence of words in a natural language (ibid-Tunstall-Pedoe, see claim 1, translation discussion, paragraphs [0024-0026]); and (ii) automatically translating that sequence of words into the machine-readable processable language by identifying or generating structured machine-readable representations that semantically represent a meaning of the sequence of words in the machine-readable processable language (ibid).
As per claim 28, claim 28 sets forth limitations similar to claim 1 and is thus rejected under similar reasons and rationale, wherein the system is deemed to embody the method, such that Tunstall-Pedoe with Mabbu with Burceanu with Lin with Byrnes make obvious a computer-based system (Tunstall-Pedoe, paragraph [0024-0026]-see his system), configured to analyse data (ibid), the computer-based system including a machine learning system, wherein the machine learning system is configured to (a) automatically translate customer reviews of products or services into a machine readable processable language (ibid-see claim 1, corresponding and similar limitation), wherein the machine learning system is configured to generate respective semantic nodes and passages that represent respective customer reviews of products or services (ibid), wherein the computer-based system is configured to: (b) store in a non-transitory storage medium a structured, machine- readable representation of data that conforms to the machine-readable processable language generated in (a) (ibid); wherein the structure, machine-readable representation of data relates to the customer reviews (ibid); in which the non-transitory storage medium further includes a stored structured, machine-readable representation of data which includes reasoning passages (ibid), wherein the reasoning passages are represented in the machine-readable processable language to represent semantics of reasoning steps (ibid-see Lin, previously cited paragraphs and Figures with reasoning passages in machine-readable processable language to represent semantics of reasoning steps, paragraphs [0081, 0003, 0021], and Byrnes, as discussed above, reasoning discussion, as mapped into the machine-readable language, as similarly motivated and combined) (c) automatically process the structured, machine-readable representation of data stored in the non-transitory storage medium in (b) (ibid) and processing at least some of the reasoning passages represented in the machine-readable processable language to represent semantics of reasoning steps, to reason, to analyse the customer reviews (ibid); in which the structured, machine-readable representation of data that conforms to the machine-readable processable language comprises semantic nodes and passages (ibid); and in which each semantic node represents a respective entity and is represented by a respective identifier (ibid); and each passage is either (i) a semantic node or (ii) a combination of semantic nodes (ibid); and where machine- readable meaning comes from choice of semantic nodes and their combination and ordering as passages (ibid).
As per claim 29, Tunstall-Pedoe with Mabbu with Burceanu with Lin with Byrnes make obvious the method of Claim 9 in which the neural network system is a deep learning system (ibid-see claim 9, Burceanu, his RNN/LSTM as deep learning).
As per claim 30, Tunstall-Pedoe with Mabbu with Burceanu with Lin with Byrnes make obvious the method of Claim 10 in which the corresponding structured machine- readable representation is a machine-readable processable language comprising semantic nodes and passages (ibid-see claims 1, 10, Tunstall-Pedoe structured machine-readable representation and semantic nodes and passages discussion).
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 LAMONT M SPOONER whose telephone number is (571)272-7613. The examiner can normally be reached 8:00 AM -5:00 PM.
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/LAMONT M SPOONER/Primary Examiner, Art Unit 2657
lms
11/14/2025