DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
2. Claims 15-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 15-21 defines a “computer program product” embodying functional descriptive material. However, the claim does not define a “non-transitory” computer program product, “non-transitory” computer-readable medium, or a “non-transitory” computer-readable memory and is thus non-statutory for that reason (i.e., “When functional descriptive material is recorded on some computer-readable medium it becomes structurally and functionally interrelated to the medium and will be statutory in most cases since use of technology permits the function of the descriptive material to be realized” – Guidelines Annex IV). The scope of the presently claimed invention encompasses products that are not necessarily computer readable, and thus NOT able to impart any functionality of the recited program. The examiner suggests amending the claim(s) to embody the program on a “non-transitory computer-readable medium” or equivalent; assuming the specification does NOT define the computer readable medium as a “signal”, “carrier wave”, or “transmission medium” which are deemed non-statutory (refer to “note” below). Any amendment to the claim should be commensurate with its corresponding disclosure.
Claim Rejections - 35 USC § 103
1. 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.
2. 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.
3. Claims 1, 2, 6-9, 13-16, 20 & 21 are rejected under 35 U.S.C. 103 as being unpatentable over Apte et al. (US 20200272435 A1 hereinafter, Apte ‘435) in view of Ahuja-Cogny et al. (US 20180322396 A1 hereinafter, Ahjua-Cogny ‘396).
Regarding claim 8; Apte ‘435 discloses a system (Fig. 1A i.e. Fig. 1A is a diagram of a system of generating computer programming using artificial intelligence. Paragraph 0040),
comprising:
a processor (Fig. 11, Processor 1125);
a memory (Fig. 11, Memory 1130) for holding programmable codec (i.e. Memory 1130 stores information and instructions for execution by processor 1125. Memory 1130 can contain various components for retrieving, presenting, modifying, and storing data. Memory 1130 can store software modules that provide functionality if executed by processor 1125. The modules can include an operating system 1160 that provides operating system functionality for computer 1105. The modules can also include artificial intelligence module 1165 that provides the learning and processing functions. Data 1175 can include training data of the multiple programming languages, information associated with the natural human language communication, languages semantics, programming languages references, domain specific contexts, programming language contextual trained data, metadata and other references. Paragraph 0179);
and wherein the programmable code includes instructions for providing a platform for operating an automation that is implemented using natural language processing (i.e. Context data is generated by processing the select natural language. The logic programming based on the context data is selected. A computing instruction is determined for the select input data using the logic programming, and the computer program including the computing instruction is generated. See Abstract);
implementing upgraded software for the platform (i.e. In an embodiment, the AI Virtual Programmer can be an intelligent virtual assistant or a software agent that assists an individual (e.g., a software developer) with developing code in the various programming languages based on specific requirement(s). Paragraph 0053);
receiving a natural language description of the automation along with a context for the automation (i.e. Systems and methods of generating a computer program using artificial intelligence module include generating logic programming by analyzing natural language in sample input data received from an external source. Select input data, which includes select natural language or a coding instruction including the select natural language, is received. Context data is generated by processing the select natural language. See Abstract)
wherein the context comprises a learned information for the automation (i.e. In an embodiment, neural network 145 determines one or more probable solutions by mapping information (such as logic programming areas, the context data, the references, the natural language processing summary and/or the metadata) in a data signal to the probable solution(s). Neural network 145 (also referred to as an “artificial neural network”) provides a “learning” functionality to artificial intelligence module 100 by classifying, grouping and/or extracting features using algorithms. Using neural network 145, artificial intelligence module 100 is configured to learn how to perform task(s) from examples and/or previously-learned information (e.g., computing instruction(s) or logic programming). Paragraphs 0095 & 0144)
and executing the automation on the upgraded software for the platform (Fig. 1B, Step 164 i.e. At 164, the artificial intelligence module generates learned logic programming information by “learning” from the generation of the output data response, and updates the knowledge repository with the learned logic programming information. Artificial intelligence module can also update the deferred learning log with the learned logic programming information in order to resolve stored output data responses that the learned logic programming information may be applied to. Paragraph 0078)
Examiner reasonably believes that Apte ‘435 discloses the limitations as expressed above. However, Examiner cites Ahjua-Cogny ‘396 to cure any deficiencies of Apte ‘435.
Ahjua-Cogny ‘396 also discloses executing the automation on the upgraded software for the platform (i.e. A knowledge process can be executed and analyzed on server systems customized for knowledge-based operations (using KPEL) or classic IT business platforms (using BPEL) Paragraph 0042)
Apte ‘435 and Ahjua-Cogny ‘396 are combinable because they are from same field of endeavor of speech systems (Ahjua-Cogny ‘396 at “Technical Field”).
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the speech system as taught by Apte ‘435 by adding a speech as taught by Ahjua-Cogny ‘396. The motivation for doing so would have been advantageous for automated document generation because current techniques can create a document where an initial document is generated with variables which are then required to be filled in to generate a case-based document. Therefore, it would have been obvious to combine Apte ‘435 with Ahjua-Cogny ‘396 to obtain the invention as specified.
Regarding claim 9; Apte ‘435 discloses wherein the learned information comprises a learned fact or a learned procedure (i.e. Artificial intelligence module stores the data file in a deferred learning log, at 422. The deferred learning log includes detailed information about events that occur when the deferred learning log is generated. The detailed information can include information such as skill details, skill-variation, type of input used, type of output generated, what is the exact input, input parameters used for skill to generate the output and so on so forth. The detailed information can also include time, place and user information. Information about the generated output and the expected output, and any other suggestion entered by the user and other related information. Paragraphs 0079 & 0131)
Regarding claim 13; Apte ‘435 discloses wherein a natural language representation of the automation corresponds to one or more ASTs (i.e. The generating of the context data includes creating metadata for the select input data by performing, using a natural language processing module, at least one of sentiment analysis, sentence recognition, relationship extraction or language detection; and generating the context data using the metadata. Paragraph 0018).
Regarding claim 14; Apte ‘435 discloses wherein the one or more ASTs comprise an automation AST, a context AST, and a dependency AST (i.e. The generating of the select context data includes creating metadata for the select input data by performing, using a natural language processing module, at least one of sentiment analysis, sentence recognition, relationship extraction or language detection; and generating the context data using the metadata. Paragraph 0026).
Regarding claim 1; Claim 1 contains substantially the same subject matter as claim 8. Therefore, claim 1 is rejected on the same grounds as claim 8.
Regarding claim 2; Claim 2 contains substantially the same subject matter as claim 9. Therefore, claim 2 is rejected on the same grounds as claim 9.
Regarding claim 6; Claim 6 contains substantially the same subject matter as claim 13. Therefore, claim 6 is rejected on the same grounds as claim 13.
Regarding claim 7; Claim 7 contains substantially the same subject matter as claim 14. Therefore, claim 7 is rejected on the same grounds as claim 14.
Regarding claim 15; Claim 15 contains substantially the same subject matter as claim 8. Therefore, claim 15 is rejected on the same grounds as claim 8. However, claim 15 further discloses a computer program product embodied on a computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor, performs operations. Paragraph 0030 of Apte ‘435 discloses a non-transitory computer readable medium having instructions embodied thereon that, when executed by a processor, cause the processor to perform operations.
Regarding claim 16; Claim 16 contains substantially the same subject matter as claim 9. Therefore, claim 16 is rejected on the same grounds as claim 9.
Regarding claim 20; Claim 20 contains substantially the same subject matter as claim 13. Therefore, claim 20 is rejected on the same grounds as claim 13.
Regarding claim 21; Claim 21 contains substantially the same subject matter as claim 14. Therefore, claim 21 is rejected on the same grounds as claim 14.
Allowable Subject Matter
1. Claims 3-5, 10-12 & 17-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 3 & 17; Claims 3 & 17 contains substantially the same subject matter as claim 10. Therefore, claim 3 &17 are objected on the same grounds as claim 10.
3. Regarding claims 4 & 18; Claims 4 & 18 contains substantially the same subject matter as claim 11. Therefore, claim 4 &18 are objected on the same grounds as claim 11.
4. Regarding claims 5 & 19; Claims 5 & 19 contains substantially the same subject matter as claim 12. Therefore, claim 5 &19 are objected on the same grounds as claim 12.
Examiners Statement of Reasons for Allowance
The cited reference (Apte ‘435) teaches systems and methods of generating a computer program using artificial intelligence module include generating logic programming by analyzing natural language in sample input data received from an external source, the sample input data resulting in a known output. Select input data, which includes select natural language or a coding instruction including the select natural language, is received. Context data is generated by processing the select natural language. The logic programming based on the context data is selected. A computing instruction is determined for the select input data using the logic programming, and the computer program including the computing instruction is generated.
The cited reference (Ahjua-Cogny ‘396) teaches a method for automating knowledge-based processes and operations includes a computer receiving an information dataset comprising knowledge and organizing the information dataset into a plurality of information elements. The computer maps the plurality of information elements into knowledge processes expressed in a knowledge process modeling language. Then, the computer converts the knowledge processes into a knowledge process executable language. Alternatively (or additionally), the computer translates the knowledge processes in a business process model language and converts the translated knowledge processes into a business process executable language.
The cited references fail to disclose wherein a first persistent store is used to hold a natural language version of the automation and a second persistent store is used to hold a non-natural language executable version of the automation; wherein a natural language representation corresponds to a book that represents the automation and associated dependencies of the automation; wherein an existing low level representation of the automation is not converted to a new low level representation for the automation, but instead the new low level representation for the automation is generated using the upgraded software for the platform. As a result, and for these reasons, Examiner indicates Claims 3-5, 10-12 & 17-19 as allowable subject matter.
Relevant Prior Art References Not Relied Upon
1. Ahjua-Cogny ‘396 et al. (US 20160335549 A1) - A method for automating knowledge-based processes and operations includes a computer receiving an information dataset comprising knowledge and organizing the information dataset into a plurality of information elements. The computer maps the plurality of information elements into knowledge processes expressed in a knowledge process modeling language. Then, the computer converts the knowledge processes into a knowledge process executable language. Alternatively (or additionally), the computer translates the knowledge processes in a business process model language and converts the translated knowledge processes into a business process executable language.
2. Han et al. (US 20200223061 A1) - A system for automating a process using robotic process automation code includes a memory device for storing program code, and at least one processor device operatively coupled to the memory device. The at least one processor device is configured to execute program code stored on the memory device to process, based on a contextual dictionary, a process description document associated with a process to be automated by a robotic process automation system, automatically generate robotic process automation code based on the processing, and execute the robotic process automation code for automating the process.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARCUS T. RILEY, ESQ. whose telephone number is (571)270-1581. The examiner can normally be reached 9-5 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hai Phan 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.
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MARCUS T. RILEY, ESQ.
Primary Examiner
Art Unit 2654
/MARCUS T RILEY/Primary Examiner, Art Unit 2654