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
1. 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. Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows. Claim 20 defines “a computer program product” embodying functional descriptive material. However, the claim does not define a “non-transitory” computer-readable medium, a “non-transitory” computer-readable memory or a “non-transitory” computer program product 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 program product” 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, 13 & 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zheng (US 20190385599 A1 hereinafter, Zheng ‘599) in view of Qadrud-Din et al. (US 20240289363 A1 hereinafter, Qadrud ‘363).
Regarding claim 13; Zheng ‘599 discloses an electronic device (Fig. 8, Electronic Device 700);
comprising: at least one processor (Fig. 8, Central Processing Unit 701);
and a memory (Fig. 8 System Memory 704) communicatively connected to the at least one processor (i.e. Fig. 8 is a schematic structural diagram of a speech recognition device. The speech recognition device 700 includes a Central Processing Unit (CPU) 701, a system memory 704 including a random access memory (RAM) 702 and a read-only memory (ROM) 703, and a system bus 705 connecting the system memory 704 and the CPU 701. Paragraph 0177);
wherein the processor is configured to: obtain a question entered by a user (i.e. The speech recognition device may send the target result to the voice receiving device. The voice receiving device obtains dialogue information according to the target result. For example, the voice receiving device is a smart speaker, and the target result is “what are you doing”. Therefore, after receiving the target result, the smart speaker generates dialogue information according to a dialogue model. Paragraph 0143 & 0178);
Zheng ‘599 does not expressly disclose the limitation as expressed below.
Qadrud ‘363 discloses wherein the question is used to instruct a generation of a text of a target type (i.e. Techniques and mechanisms described herein may provide for automated solutions for generated text in accordance with a number of specialized applications. Such applications may include, but are not limited to: simplifying language, generating correspondence, generating a timeline, reviewing documents, editing a contract clause, drafting a contract. Paragraph 0037);
obtain, from a plurality of preset sets of rules, a set of target rules corresponding to the target type, wherein the set of target rules comprises a plurality of target rules, and the target rules are rules to be followed by the text of the target type (i.e. A text generation interface system may take as input one or more arbitrary documents, process them via optical text recognition, segment them into portions, and process the segmented text via various tasks based on need. Different workflows are provided for different tasks, and this application describes a number of examples of such workflows. In many workflows, an input document is divided into chunks via a chunking technique. Then, chunks are inserted into prompt templates for processing by a large language model such as the GPT-3 or GPT-4 available from OpenAI. Paragraph 0025);
and input the plurality of target rules sequentially into a large language model according to a sequence of the plurality of target rules to obtain a target text of the target type generated by the large language model (i.e. Large language models often receive as input a portion of input text and generate in response a portion of output text. In many systems, the large language model imposes a limit on the input text size. Accordingly, in the event that the large language model is asked to summarize a length document, the document may need to be segmented into portions in order to achieve the desired summarization. Paragraph 0028);
Zheng ‘599 and Qadrud ‘363 are combinable because they are from same field of endeavor of speech systems (Qadrud ‘363 at “Field of Technology”).
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 Zheng ‘599 by adding the limitation as taught by Qadrud ‘363. The motivation for doing so would have been advantageous because it would provide improved techniques for accessing information encoded in natural language. Therefore, it would have been obvious to combine Zheng ‘599 with Qadrud ‘363 to obtain the invention as specified.
Regarding claim 18; Qadrud ‘363 discloses wherein input the target rules into the large language model sequentially according to the sequence of the target rules to obtain the target text of the target type generated by the large language model, comprises: verify whether the target text follows the target rules according to the sequence of the target rules (i.e. The text may be associated with a sequence of text chunks. The text portions selected at 506 and identified at 512 may be assigned to these text chunks, for instance in a sequential order. That is, text portions near to one another in the text itself may be assigned to the same text chunk where possible to reduce the number of divisions between semantically similar elements of the text. Paragraph 0095)
and in response to the target text following the plurality of target rules, display the target text to the user (i.e. A computer system or computing device may include or communicate with a monitor, printer, or other suitable display for providing any of the results mentioned herein to a user. Paragraph 0113)
Regarding claim 1; Claim 1 contains substantially the same subject matter as claim 13. Therefore, claim 1 is rejected on the same grounds as claim 1.
Regarding claim 19; Claim 19 contains substantially the same subject matter as claim 13. Therefore, claim 19 is rejected on the same grounds as claim 13. However, claim 19 further discloses a non-transitory computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are used to cause a computer to implement the method for optimizing content generated by a large model. Zheng ‘599 discloses at Paragraph 0007 wherein there is provided one or more non-transitory storage mediums storing computer readable instructions, the computer readable instructions, when executed by one or more processors, causing the one or more processors to execute the method.
Regarding claim 20; Zheng ‘599 discloses a computer program product comprising a computer program, wherein when the computer program is executed by a processor, the method of claim 1 is implemented (i.e. There is provided one or more non-transitory storage mediums storing computer readable instructions, the computer readable instructions, when executed by one or more processors, causing the one or more processors to execute the method. Paragraph 0007).
Allowable Subject Matter
1. Claims 2-12 & 14-17 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.
2. Claims 3-5 depend on indicated objected claim 2. Therefore, by virtue of their dependency, Claims 3-5 are also indicated as objected subject matter.
3. Claim 7 depend on indicated objected claim 6. Therefore, by virtue of its dependency, Claim 7 is also indicated as objected subject matter.
4. Claims 9-11 depend on indicated objected claim 8. Therefore, by virtue of their dependency, Claims 9-11 are also indicated as objected subject matter.
5. Claims 15-17 depend on indicated objected claim 14. Therefore, by virtue of their dependency, Claims 15-17 are also indicated as objected subject matter.
Examiners Statement of Reasons for Allowance
The cited reference (Zheng ‘599) teaches wherein a speech recognition method is provided. The method includes: obtaining a voice signal; processing the voice signal according to a speech recognition algorithm to obtain n candidate recognition results, the candidate recognition results including text information corresponding to the voice signal; identifying a target result from among the n candidate recognition results according to a selection rule selected from among m selection rules, the selection rule having an execution sequence of j, the target result being a candidate recognition result that has a highest matching degree with the voice signal in the n candidate recognition results, an initial value of j being 1; and identifying the target result from among the n candidate recognition results according to a selection rule having an execution sequence of j+1 based on the target result not being identified according to the selection rule having the execution sequence of j.
The cited reference (Qadrud ‘363) teaches wherein a query request may be received via a communication interface. Records may be retrieved from a database system based on the query request. The records may correspond with document portions selected from documents. A subset of the records may be determined by applying textual analysis of the document portions based on the query request. A response message to the original request may be generated and sent via a communication interface. The response message may include an answer to the query in natural language generated based on the first subset of the records.
The cited references fail to disclose wherein the step of inputting the plurality of target rules sequentially into the large language model according to the sequence of the plurality of target rules to obtain the target text of the target type generated by the large language model, comprises: in response to the number of the plurality of target rules included in the set of target rules being greater than or equal to a preset number, inputting the plurality of target rules into the large language model sequentially according to the sequence of the plurality of target rules to obtain the target text of the target type generated by the large language model; wherein inputting the plurality of target rules sequentially into the large language model according to the sequence of the plurality of target rules to obtain the target text of the target type generated by the large language model, comprises: verifying whether the target text follows the plurality of target rules according to the sequence of the plurality of target rules; and in response to the target text following the plurality of target rules, displaying the target text to a user; wherein obtaining the set of target rules corresponding to the target type from the plurality of preset sets of rules, comprises: determining a set of rules selected by a user from the plurality of preset sets of rules as the set of target rules; or, performing text classification on the question to obtain a target type to which a generated text instructed by the question belongs; and obtaining the set of target rules corresponding to the target type based on a correspondence between the plurality of preset sets of rules and preset types; wherein the set of target rules comprises a plurality of nodes connected in series, and the plurality of nodes comprise a rule node representing one of the target rules, and input the plurality of target rules sequentially into the large language model according to the sequence of the plurality of target rules to obtain the target text of the target type generated by the large language model, comprises: according to a sequence of the plurality of nodes, perform the following steps for each node: in response to the node being the rule node, generate first prompt information based on a target rule represented by the node; in response to the node being the first node included in the set of target rules, input the first prompt information and the question into the large language model to obtain an output result of the large language model; in response to the node not being the first node, input the first prompt information and an output result obtained based on the previous node into the large language model to obtain an output result of the large language model; and determine a final output result as the target text, wherein the final output result is an output result obtained based on the last node included in the set of target rules. As a result, and for these reasons, Examiner indicates Claims 2-12 & 14-17 as allowable subject matter.
Relevant Prior Art References Not Relied Upon
1. Cooper et al. (US 20130041921 A1) - A system, method, and computer readable medium for applying one or more information retrieval technologies is disclosed for resolving a query. In one embodiment, an exemplary system generates a response using a language analysis module configured to parse a query into elements. This system can also include a rules engine configured to compare a condition of a rule against the elements, where the rule is configured to perform an action to retrieve information. Further, a response generator coupled to said rules engine receives the rule and retrieves the information for presentation to a querier in a portion of a display that adjusts proportionately to the degree of importance of said information.
2. Lang et al. (US 20200286318 A1) - Disclosed are a method, an electronic device, and a computer readable storage medium for creating a vote. The method includes: receiving a vote initiation instruction to obtain voice information inputted currently; determining textual information by recognizing the voice information; determining at least two option words based on semantic recognition of the textual information; generating a question stem of the vote based on the textual information; and generating voting options based on the option words for the vote.
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
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