CTNF 18/814,787 CTNF 89908 Notice of Pre-AIA or AIA Status 07-03-fti AIA The present application is being examined under the pre-AIA first to invent provisions. DETAILED ACTION The office action sent in response to Applicant’s communication received on 8/26/2024 for the application number 18814787. The office hereby acknowledges receipt of the following placed of record in the file: Specification, Abstract, Oath/Declaration and claims. Priority This application is a continuation of U.S. Patent Application No. 16/410,641, filed May 13, 2019, now U.S. Patent No. 12,072,877, which is a continuation of U.S. Patent Application 14/233,640, filed September 18, 2014, now U.S. Patent No. 10,387,410, which is a U.S. National Stage Application of PCT/CA2012/000685, filed July 19, 2012, which claims priority to and the benefit of U.S. Provisional Patent Application No. 61/596,407, filed February 8, 2012, and Canadian Patent Application No. 2,747,153, filed July 19, 2011 Status of the claims Claims 1-26 are cancelled. Claims 27-46 are newly added. Claims 27-46 are presented for examination. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 27-31, 35-39 and 43-45 are rejected under 101. Claim 36 includes: A computing device comprising: at least one processor; and at least one memory storing computer-executable instructions that, when executed by the at least one processor, cause the computing device to: (a) receive user query indicative of a command for controlling a user interface; (b) in response to a type of the user query being a functional query, determining candidate commands based on the user query; select a particular command from the candidate commands using a plurality of natural language processing techniques;(c) generate a feature vector representing the user query and relevant information associated with the user query; (d) extract one or more entities required by the particular command from the feature vector; and (e) in response to identification of all entities required by the particular command, perform the particular command. Steps (a) –(b) and (d) and (e) can be performed by the mentally since human can receive a command, determine the intend and choose a list of commands which can be used to invoke an action and can perform the command based on the entities within the command Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least a device hence a machine. Thus, the claim is , recites a statutory categories of invention. (Step 1: YES). Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. As discussed above, the broadest reasonable interpretation of steps (a)-(b) , (d) and (e) that those steps fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. As discussed human can receive a command, determine the intend and choose a list of commands and previous context from the other human which can be used to invoke an action and can perform the command based on the entities within the command. Hence, these steps can be performed by a human, using “observation, evaluation, judgment, [and] opinion,” because they involve making doing analysis on the given data which are mental tasks humans routinely do,' ” and thus can practically be performed in the human mind, In re Killian, 45 F.4th 1373, 1379 (Fed. Cir. 2022). Therefore, these limitations are considered together as a abstract idea for further analysis. (Step 2A, Prong One: YES). Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Claim requires memory, processor, and (c) generate a feature vector representing the user query and relevant information associated with the user query. The limitations of using a memory and processor provide nothing more than mere instructions to implement an abstract idea on a generic computer. The limitation of generating a feature vector can be done is a computer component and can using any generic computer and is needed to perform to the abstract idea. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to 8 perform an existing process; and (3) the particularity or generality of the application of the judicial exception Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES). Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. At Step 2A, Prong Two, the second additional element of using memory, processor, generating a feature vector was found to represent no more than mere instructions to apply the judicial exception on a computer using generic computer components. The analysis under Step 2A, Prong Two is carried through to Step 2B. Therefore, this limitation remains insignificant extra solution activity even upon reconsideration and does not amount to significantly more. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept (Step 2B: NO). The claim is not eligible. Regarding claims 28-31, 37-39 and 45 recites a combination of mental process as same in claim 17 and additional element which are mere generic computer component under step 2, prong 2A and well known routine and conventional under prong 2b. Hence these claims are patent ineligible. Regarding claim 27 and 44, analysis applicable to claim 36, are applicable Regarding claim 1, analysis analogous to claim 11, are applicable. Additionally, Claim 44-46 is drawn to " A computer storage medium " which includes both statutory and non-statutory subject matter. Under the broadest reasonable interpretation of the claim drawn to A computer storage medium as per as the specification (Para 0014) includes the signal and embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. Specifically, as per the specification para 0014 (for e.g. there is provided a computer program product comprising a storage medium (e.g. a memory or other storage device) storing instructions and data for performing a method in accordance with an aspect described. ) .Computer storage does not exclude the signal. And therefore because of the broadest reasonable interpretation computer readable storage medium covers a signal per se, a rejection under 35 USC 101 is appropriate as covering non-statutory subject matter. The examiner suggest applicant to amend by inserting a word ---non-transitory-- prior to "computer program product" to overcome rejections under 35 U.S.C § 101. Examiner’s Remark Claims 32-34, 40-42 and 46 requires a specific algorithm to determine and optimize user query. When viewed in the ordered combination, the claim improves the technology of "recognizing user query", hence patent eligible under abstract idea analysis Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13. The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer. 08-34 Claims 27-46 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-23 of US patent application 10387410 . Although the claims at issue are not identical, they are not patentably distinct from each other. Regarding claim 27-46, claims 1-23 of US Patent application US 10387410 claims all the limitations set forth in the application claim 27-46. Although the claims are not exactly same the patent application reads on the current application and has additional limitations. 08-34 Claims 27-46 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent US 12072877 . Although the claims at issue are not identical, they are not patentably distinct from each other. Regarding claim 27-46 , claims 1-18 of U.S. Patent US 12072877 claims all the limitations set forth in the application claim 27-46. Although the claims are not exactly same the U.S. Patent US 12072877 reads on the current application and has additional limitations. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. And KSR, 550 U.S. at 418, 82 USPQ2d at 1396. Exemplary rationales that may support a conclusion of obviousness include: (A) Combining prior art elements according to known methods to yield predictable results; (B) Simple substitution of one known element for another to obtain predictable results; (C) Use of known technique to improve similar devices (methods, or products) in the same way; (D) Applying a known technique to a known device (method, or product) ready for improvement to yield predictable results; (E) "Obvious to try" – choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success; (F) Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art; (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. See MPEP § 2143 for a discussion of the rationales listed above along with examples illustrating how the cited rationales may be used to support a finding of obviousness. See also MPEP § 2144 - § 2144.09 for additional guidance regarding support for obviousness determination. 07-21-aia AIA Claim s 27-29, 36-38 and 44 are rejected under 35 U.S.C. 103 as being unpatentable over Katariya ( US 20070124263) and further in view of Ross (US 20020133355 ) Regarding claim 1, Katariya teaches method for processing user queries for performing one or more commands, the method comprising: receiving a user query indicative of a command for controlling a user interface ( perform an action or displays an action based on query, Para 0028-0029, 0034) ; a type of the user query is a functional query ( query, statement etc., Para 0032-0033, 0077) , determining candidate commands based on the user query ( predicted task, Fig 5-6) ; selecting a particular command from the candidate commands using a plurality of natural language processing techniques ( based on user feedback select, Para 0060, 0065-0066) ; generating a feature vector representing the user query and relevant information associated with the user query ( A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be performed, Para 0073; also using user context, Para 0077) ; extracting one or more entities required by the particular command from the feature vector (maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be performed. In the case of natural language systems, for example, attributes can be words or phrases or other data-specific attributes derived from the words (e.g., database tables, the presence of key terms), and the classes are categories or areas of interest (e.g., levels of priorities), Para 0073; words, phrases, terms can be named entities, Para 0037)) ; and in response to identifying all entities required by the particular command, performing the particular command ( perform the action, Fig 8) Katariya does not explicitly teach in response to determining that a type of the user query is a functional query, determining commands based on the user query Ross in the same field of endeavor teaches in response to determining that a type of the user query is a functional query, determining commands based on the user query ( In step 104, the conversation manager 28 of the speech center system 20 generates one or more responses 76 to the spoken input 14. In one example of the process of step 104, the conversation manger 28 processes the recognized spoken utterance 15 to produce an internal utterance representation. The conversation manager 28 processes the utterance representation through the reasoning facility 52 to produce one or more responses 76 (e.g., a question, message, announcement, or other response for the user of the computer 10; and respond to the user based on the dialog goals, Para 0061-0062, 0094, Fig 5) It would have been obvious to combine the Katariya concept with Ross’s teachings at the time of invention to ensure the user remains in control by conforming to the natural "rhythm" of conversation, such as allowing adequate time between utterances and taking "turns" in a dialog. This is achieved by having the dialog manager decide between invoking the disambiguator method (for disambiguation goals), the verifier method (for verification or Boolean proposition goals), the corrector method (for correction goals) or the inquisitor method (for slot-name questions about variable proposition goals) ( Para 0006-0007, 0062, Ross) Regarding claim 28, Katariya modified by Ross as above in claim 27, teaches wherein the type of the user query selected from a group comprising a functional query, an entity query, and a clarification query ( functional query for e.g. command leads to an action, Para 0028-0029, 0034, Katariya; The dialog manager 56 recognizes a variety of question types, including disambiguation (choosing between alternatives), verification (a yes/no question to validate a command or information), correction (to obtain a different response when a user gives incorrect commands or information), and inquisition (obtaining missing information). Responses to each of these question types are handled by the dialog manager 56, based on the recognition context, Para 0061-0062) Regarding claim 29, Katariya modified by Ross as above in claim 27wherein the relevant information associated with the user query ( feedback from prior queries, Para 0077, Katariya ) comprises a previously initiated clarification question ( dialog flow including the yes/no question and using the records which can have yes/no questions, Para 0062, 0096 -0097, Fig 5,Ross) Regarding claim 36, arguments analogous to claim 27, are applicable. In addition, Katariaya teaches A computing device comprising: at least one processor; and at least one memory storing computer-executable instructions that, when executed by the at least one processor, cause the computing device to perform the steps as recited in claim 27 ( fig 3-5) Regarding claim 37,arguments analogous to claim 28, are applicable. Regarding claim 38,arguments analogous to claim 29, are applicable. Regarding claim 44, arguments analogous to claim 27, are applicable. In addition, Katariya teaches A computer storage medium storing computer-executable instructions that, when executed by at least one processor, cause a computing device to perform the steps as recited in claim 27 ( fig 3-5, Para 0026) 07-21-aia AIA Claims 30-34, 39-42 and 46 and are r ejected under 35 U.S.C. 103 as being unpatentable over K atariya ( US 20070124263) and further in view of Ross (US 20020133355 ) and further in view of Hasanuzzaman ( Feature Subset Selection Using Genetic Algorithm for Named Entity Recognition) R egarding claim 30, Katariya as above in claim 27, teach defining feature sets with which to extract entities for particular types of commands ( maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to prognose or infer an action that a user desires to be performed. In the case of natural language systems, for example, attributes can be words or phrases or other data-specific attributes derived from the words (e.g., database tables, the presence of key terms), and the classes are categories or areas of interest (e.g., levels of priorities), Para 0073; words, phrases, terms can be named entities, Para 0037) Does not teach defining optimized feature sets with which to extract entities for an operation using a genetic algorithm However, Hasanuzzaman teach defining optimized feature sets with which to extract entities for particular types of commands using a genetic algorithm (The main features for the NER task are identified based on the different possible combinations of available word and tag contexts….. feature 1-8; GA based technique, Under 2 Named Entity Features and 3. Proposed Approach) It would have been obvious having the teachings of Katariya and Ross to further include the concept of Hasanuzzaman at the time of invention since using genetic algorithm improved performance to determine the entities ( Under Introduction, Page 154- 2-3 rd para, Hasanuzzaman) Regarding claim 31, Hasanuzzaman as above in claim 30, teach wherein the optimized feature set comprising one or more feature vectors ( feature vector, Fig 1 and selection, Fig 2) Regarding claim 32, Hasanuzzaman as above in claim 30, teach wherein defining optimized feature sets comprises: performing at least one round of genetic selection to identify the optimized feature sets (output best chromosome and stop, Fig 2) , wherein one round of genetic selec---tion comprises: identifying at least one initial feature set ( initial population, Fig 2, Under 3. Proposed Approach) ; generating a plurality of random permutations from the at least one initial feature set ( mutation, 3.5) ; testing the plurality of random permutations using a dataset of test querie s ( fitness computation, Under 3) ; determining a performance measure for each of the plurality of random permutations ( performance, Fig 2) ; and selecting a subset of the plurality of random permutations at least in part based on the performance measure of each respective random permutation ( output the best chromosome and stop, Fig 2, Under 3) Regarding claim 33, Hasanuzzaman as above in claim 32, teach wherein the performance measure comprises an accuracy measure and a computational efficiency measure ( Recall, Precision, and the F-measure (also called F1-score) to evaluate accuracy—examples are table 3-5) Regarding claim 34, Hasanuzzaman as above in claim 32, teach performing additional rounds of genetic selection after a first round of genetic selection has been performed, each additional round of genetic selection including the step of setting the subset of the plurality of random permutations as the at least one initial feature set ( 5. Steps 2 and 3 are repeated 3 times to perform 3-fold cross validation., Under 3. Proposed Approach) Regarding claim 39,arguments analogous to claim 31, are applicable. Regarding claim 40,arguments analogous to claim 32, are applicable. Regarding claim 41,arguments analogous to claim 33, are applicable. Regarding claim 42,arguments analogous to claim 34, are applicable. Regarding claim 45,arguments analogous to claim 31, are applicable. Regarding claim 46,arguments analogous to claim 32, are applicable . 07-21-aia AIA Claim s 35 and 43 are rejected under 35 U.S.C. 103 as being unpatentable over Katariya ( US 20070124263) and further in view of Ross (US 20020133355 ) and further in view of (US 7613663) Regarding claim 35, Katariya as above in claim 27 mentions wherein selecting the particular command from the candidate commands comprises selecting the particular command from the candidate commands using neural network responsive to an output of each of the plurality of natural language processing techniques ( using a neural network, Para 0074) Katariya modified by Ross does not explicitly teaches using a two layer neural network responsive to an output of each of the plurality of natural language processing techniques However, Commons teaches using a two layer neural network responsive to an output of each of the plurality of natural language processing techniques (feed forward neural network for language processing and generate output based on command, ( col 15, line 30-55; col 16, line 20-40) In the same field on endeavor obvious having the teachings of Katariya and Ross to further include the concept of Commons as the time of invention to improve task performance ( col 2, line 10-15, Commons) Regarding claim 43,arguments analogous to claim 35, are applicable . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cooper ( US Pub: 20090077047 ) Varma ( US Pub: 20120254143) Tankovich(US 20090259651) Kennewick (US 20090171664) teaches method for processing user queries for performing one or more commands, the method comprising: receiving a user query indicative of a command for controlling a user interface (for e.g. command include “playing music” or any other action, Para 0035-0042) ; in response to determining that a type of the user query is a functional query, determining candidate commands based on the user query (format the command based on user query, Para 0186) ; selecting a particular command from the candidate commands using a plurality of natural language processing techniques (selecting the formatted command, Para 0186-0187) ; generating a feature vector ( using the fuzzy logic and probabilities – here the raw data is converted to feature vector, Para 0186-0189) representing the user query and relevant information associated with the user query ( location/date time, type of query for e..g flight information vs portfolio etc., Para 0186-0189) ; extracting one or more entities required by the particular command from the feature vector ( for e.g. keyword stock portfolio or flight information) ; and in response to identifying all entities required by the particular command, performing the particular command ( give the results, Para 0186-0189) Any inquiry concerning this communication or earlier communications from the examiner should be directed to Richa Sonifrank whose telephone number is (571)272-5357. The examiner can normally be reached M-T 7AM - 5:30PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Phan Hai can be reached at (571)272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Richa Sonifrank/Primary Examiner, Art Unit 2654 Application/Control Number: 18/814,787 Page 2 Art Unit: 2654 Application/Control Number: 18/814,787 Page 3 Art Unit: 2654 Application/Control Number: 18/814,787 Page 4 Art Unit: 2654 Application/Control Number: 18/814,787 Page 5 Art Unit: 2654 Application/Control Number: 18/814,787 Page 6 Art Unit: 2654 Application/Control Number: 18/814,787 Page 7 Art Unit: 2654 Application/Control Number: 18/814,787 Page 8 Art Unit: 2654 Application/Control Number: 18/814,787 Page 9 Art Unit: 2654 Application/Control Number: 18/814,787 Page 10 Art Unit: 2654 Application/Control Number: 18/814,787 Page 11 Art Unit: 2654 Application/Control Number: 18/814,787 Page 12 Art Unit: 2654 Application/Control Number: 18/814,787 Page 13 Art Unit: 2654 Application/Control Number: 18/814,787 Page 14 Art Unit: 2654
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