Detailed Action
This communication is in response to the Arguments and Amendments filed on 4/2/2026.
Claims 1-5, 7-13 are pending and have been examined.
Claim 6 has been cancelled.
Claims 1-5, 7-13 are rejected. Hence, this action has been made Final.
Claims 1, 9, and 10 are independent are system, method, and storage medium claims, respectively.
Apparent priority: 6/20/2023.
Any previous objection/rejection not mentioned in this Office Action has been withdrawn by the Examiner.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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
Arguments and Amendments
Applicant has amended the independent claims to include “1. (Currently Amended) An information processing apparatus, comprising at least one processor, the at least one processor carrying out: an identification process of identifying a work category of [[a]] one or more target sentences; a generation process of generating a prompt that corresponds to the work category which has been identified in the identification process; and an acquisition process of acquiring a generation sentence which has been generated based on the prompt, wherein each of a plurality of work categories is associated with one or more elements of a sentence by predefined association information, wherein in the generation process, the at least one processor performs: determining, based on the predefined association information, the one or more elements corresponding to the work category identified in the identification process; carrying out an extraction process of extracting, from the one or more target sentences, the determined one or more elements corresponding to the identified work category by using a pretrained work-category-specific model corresponding to the identified work category, wherein the work-category-specific model is prepared for each work category; and generating the prompt corresponding to the identified work category by applying content of an instruction sentence and content of the extracted one or more elements to a predetermined prompt template, wherein the predetermined prompt template includes a tag indicating the instruction sentence and a tag indicating an input sentence, and wherein the generated prompt is configured by associating the content of the instruction sentence with the tag indicating the instruction sentence, and associating the content of the extracted one or more elements with the tag indicating the input sentence.”
Regarding the Rejections under 35 U.S.C. 101 Applicant notes
Claim 1 recites, inter alia, carrying out an extraction process of extracting, from the one or more target sentences, the determined one or more elements corresponding to the identified work category by using a pretrained work-category-specific model corresponding to the identified work category, wherein the work-category-specific model is prepared for each work category. Thus, the rejection of claim 6 is predicated on the contention that the extraction process relates to a human extracting elements from a sentence using pen and paper. However, amended claim 1 recites that the extraction process uses a pretrained work-category-specific model corresponding to the identified work category, wherein the work-category-specific model is prepared for each work category. Applicant respectfully submits that the extraction process is not capable of being performed by a human using pen and paper.
Examiner notes the amended claim language is largely high level and functional: “work-category-specific model is prepared for each work category” — these can be reasonably characterized as information processing/mental like steps (a human can prepare for a work category). Absent claim detail tying the operations to specific technical mechanisms that go beyond mere data processing, these limitations are susceptible to classification as mental/data manipulation concepts
Naming a model without structure or constraints does not automatically transform an abstract information processing claim into a patent eligible technological improvement.
Applicant notes/disagrees with the contention that claim 1 recites the use of a generic computer component. For example, as amended, claim 1 recites that the extraction process uses a pretrained work- category-specific model corresponding to the identified work category, wherein the work- category-specific model is prepared for each work category. Applicant respectfully submits that one of ordinary skill in the art would understand a pretrained work-category-specific model to be a non-generic processing component. Further to the above, current U.S. case law states that method claims that merely require generic computer implementation fail to transform that abstract idea into a patent-eligible invention (see, e.g., Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 223 (2014), citing Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66 (2012)). However, claim 1 does not recite a generic computer implementation. Rather, claim 1 recites, inter alia, the use of a specific, pretrained work-category-specific model.
Examiner notes Without claim specificity tying those components to particular unconventional architectures, constrained parameterizations, training/regimen steps, or demonstrable improvements, the recited elements appear to be routine, conventional uses of a model and generic software components, and therefore fail to supply an inventive concept.
Applicant notes claim 3 recites that the work category includes work to generate or edit a contract. Thus, in relation to claim 3, the work-category-specific model is prepared for work to generate or edit a contract. Such a model is not capable of being performed by a human, nor is it a generic computing component.
Examiner notes that a human can generate or edit a contract according to a work category using pen and paper. Without sufficient specificity or claim detail regarding how the model is trained i.e. showing how it is unique or unconventional - the model is noted as a generic model and therefore fails to supply an inventive concept.
Applicant notes claim 4 recites that work category includes work to check a contract. Thus, in relation to claim 4, the work-category-specific model is prepared for work to check a contract. Such a model is not capable of being performed by a human, nor is it a generic computing component.
Examiner notes that a human can check a contract. Without sufficient specificity or claim detail regarding how the model is trained i.e. showing how it is unique or unconventional - the model is noted as a generic model and therefore fails to supply an inventive concept.
Regarding the Rejections under 35 U.S.C. § 103 Applicant notes
Applicant notes that the Office failed to establish a prima facie case of obviousness at least because the Office failed to properly determine the differences between the prior art and the claims based on whether the claimed subject matter, as a whole, would have been obvious. Applicant further submits that the rejected claims are not obvious for at least the additional reasons set forth below.
Applicant notes Qadrud-Din teaches, at most, storing "information extracted from natural language documents" and/or "metadata information about documents based on information extracted from those documents." The Office Action fails to explain how Qadrud-Din's extracted information relates to "one or more elements corresponding to the work category which has been identified in the identification process," as recited in claim 1. As such, Applicant respectfully submits that the Office Action fails to set forth a prima facie case of obviousness regarding the subject matter of original claim 6 that is now recited in claim 1.
Examiner notes YOSHIKAWA has been added to the claim mapping to teach this limitation. Please see updated mappings below.
Examiner further notes Qadrud-din as containing extraction (30:64-31:10:8) for the purposes of performing the task of identifying and retrieving contractual information (31:21-26)(work category) and answer to the question (31:15-16)(prompt).
Applicant notes Qadrud-Din says nothing whatsoever about an extraction process. The entirety of Qadrud- Din's disclosure relating to the "extracted information" is set forth above in section A regarding the lack of a prima facie case. Although Qadrud-Din describes storing extracted information, Qadrud-Din does not describe how the information is extracted.
Examiner notes YOSHIKAWA has been added to the claim mapping to teach this limitation. Please see updated mappings below.
However, examiner also notes that Qadrud-din contains (30:64-31:10:8) “(199) An input metadata extraction prompt is determined at 1310 based on the text chunk and a clause splitting prompt template. In some embodiments, the input metadata extraction prompt may be determined by supplementing and/or modifying the input metadata extraction prompt based on the one or more clauses and the one or more data fields. For instance, the one or more clauses and a description of the one or more data fields may be added to a prompt template at an appropriate location. As one example, a prompt template may include a set of instructions for causing a large language model to identify values for the one or more data fields based on the one or more clauses.”
Examiner further notes Qadrud-din as containing extraction (30:64-31:10:8) for the purposes of performing the task of identifying and retrieving contractual information (31:21-26)(work category) and answer to the question (31:15-16)(prompt).
Applicant notes Claim 1 presently recites that the extraction process uses a pretrained work-category- specific model corresponding to the identified work category, wherein the work-category-specific model is prepared for each work category. Qadrud-Din fails to disclose or even suggest such an approach. Further, Hamze fails to cure the above-noted deficiencies in the teachings of Qadrud-Din relative to claim 1.
Examiner notes YOSHIKAWA has been added to teach this newly added limitation. Please see updated mappings to the independent claims below.
Applicant’s amendments, with respect to the rejection(s) Claims 1-5, 7-13 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Qadrud-Din (U.S. Patent Number US 11860914 B1), in view of Hamze (U.S. Patent Number US 20210303369 A1) and further in view of YOSHIKAWA (U.S. Patent Number US 20220391596 A1).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The independent Claims are directed to statutory categories:
Claim 1 is an apparatus claim and directed to the machine or manufacture category of patentable subject matter.
Claim 9 is a method claim and directed to the process category of patentable subject matter.
Claim 10 is a storage medium claim and is directed to the machine or manufacture category of patentable subject matter.
Regarding Independent Claim 1, Claim 1 recites,
“1. An information processing apparatus, comprising at least one processor, the at least one processor carrying out: an identification process of identifying a work category of one or more target sentences; [This relates to a human identifying a work category using observation in the human mind.]
a generation process of generating a prompt that corresponds to the work category which has been identified in the identification process; [This relates to a human generating a prompt using pen and paper.]
and an acquisition process of acquiring a generation sentence which has been generated based on the prompt. [This relates to a human acquiring a generation sentence through the visual system, generating a sentence using pen and paper.]
wherein each of a plurality of work categories is associated with one or more elements of a sentence by predefined association information, wherein in the generation process, the at least one processor performs: [This relates to a human associating elements with information using logic and reasoning.]
determining, based on the predefined association information, the one or more elements corresponding to the work category identified in the identification process; [This relates to a human determining elements corresponding to a work category using logic and reasoning.]
carrying out an extraction process of extracting, from the one or more target sentences, the determined one or more elements corresponding to the identified work category by using a pretrained work-category-specific model corresponding to the identified work category, [This relates to a human extracting an element from a sentence using pen and paper.]
wherein the work-category-specific model is prepared for each work category; and [This relates to a human preparing for a work category using pen and paper.]
generating the prompt corresponding to the identified work category by applying content of an instruction sentence and content of the extracted one or more elements to a predetermined prompt template, [This relates to a human generating a prompt using pen and paper.]
wherein the predetermined prompt template includes a tag indicating the instruction sentence and a tag indicating an input sentence, and [This relates to a human tagging a sentence using pen and paper.]
wherein the generated prompt is configured by associating the content of the instruction sentence with the tag indicating the instruction sentence, and associating the content of the extracted one or more elements with the tag indicating the input sentence. [This relates to a human making associations in the human mind.]
The Dependent Claim does not include additional limitations that could incorporate the abstract idea into a practical application or cause the Claim as a whole to amount to significantly more than the underlying abstract idea.
Regarding Independent Claim 9, Claim 9 is a Method claim with limitations similar to that of claim 1 and is rejected under the same rationale.
Regarding Independent Claim 10, Claim 10 is a Storage Medium claim with limitations similar to that of claim 1 and is rejected under the same rationale.
This judicial exception is not integrated into a practical application. In particular, claims 1, 9 and 10 recites additional elements of “storage” and “processor” For example, in [0103] of the as filed specification, there is description of using The computer C includes at least one processor C1 and at least one memory C2. The memory C2 stores a program P for causing the computer C to function as the information processing apparatus (1, 100). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using a device is noted as a general computer. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Further, the additional limitation in the claims noted above are directed towards insignificant solution activity. The claims are not patent eligible.
Dependent claim 2 recites,
“2. The information processing apparatus according to claim 1, wherein: in the generation process, the at least one processor generates the prompt which includes content corresponding to an instruction from a user. [This relates to a human generating a prompt using pen and paper.] No additional limitations present.
Dependent claim 3 recites,
“3. The information processing apparatus according to claim 2, wherein: the work category includes work to generate or edit a contract; and the prompt includes an instruction to generate or edit a contract. [This relates to a human generating or editing a contract using pen and paper.] No additional limitations present.
Dependent claim 4 recites,
“4. The information processing apparatus according to claim 2, wherein: the work category includes work to check a contract; and the prompt includes an instruction to check a contract. [This relates to a human checking a contract using logic and reasoning in the human mind.
Dependent claim 5 recites,
“5. The information processing apparatus according to claim 2, wherein: the at least one processor further carries out a revision process of revising the generation sentence in accordance with the work category which has been identified in the identification process. [This relates to a human revising a sentence using pen and paper] No additional limitations present.
Dependent claim 6 recites,
“6. The information processing apparatus according to claim 5, wherein: in the generation process, the at least one processor carries out an extraction process of extracting, from one or more target sentences, one or more elements corresponding to the work category which has been identified in the identification process, [This relates to a human extracting elements from a sentence using pen and paper.] No additional limitations present.] and the at least one processor generates the prompt corresponding to the one or more elements which have been extracted in the extraction process. [This relates to a human generating a prompt using pen and paper.] No additional limitations present.
Dependent claim 7 recites,
“7. The information processing apparatus according to claim 6, wherein: in the generation process, the at least one processor generates the prompt which includes (i) an instruction sentence corresponding to the instruction from the user and (ii) the one or more elements which have been extracted in the extraction process. [This relates to a human generating a prompt using pen and paper.] No additional limitations present.
Dependent claim 8 recites,
“8. The information processing apparatus according to claim 7, wherein: in the generation process, the at least one processor carries out the extraction process with use of a language model which has been trained for each work category. [This relates to a human extracting from language using pen and paper] No additional limitations present.
Dependent claim 11 recites,
“11. (New) The information processing apparatus according to claim 1, wherein in the extraction process, the at least one processor automatically extracts the determined one or more elements by executing an industry-type-specific model which has been trained by machine learning specific to the identified work category. [This relates to a human extracting the determined one or more elements from language using pen and paper] No additional limitations present.
Dependent claim 12 recites,
“12. (New) The information processing apparatus according to claim 1, wherein the acquisition process includes transmitting the generated prompt to a generation apparatus that executes a large language model via a communication network, and receiving the generation sentence output from the large language model. [This relates to a receiving a generation sentence using pen and paper] LLM is noted as additional limitation.
Dependent claim 13 recites,
“13. (New) The information processing apparatus according to claim 1, wherein the work category includes checking a contract, wherein the predetermined prompt template further includes a point to be checked and a tag indicating the point, [This relates to a human indicating a point using pen and paper] No additional limitations present. wherein the generated prompt is further configured by associating content of the point with the tag indicating the point to instruct outputting an itemized evaluation result, and [This relates to a human associating content of the point with the tag using logic and reasoning] wherein the at least one processor further carries out a revision process of revising the generation sentence based on the itemized evaluation result for the point. [This relates to a human revising a sentence using pen and paper] No additional limitations present.
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 1-5, 7-13 are rejected under 35 U.S.C. 103 as being unpatentable over Qadrud-Din (U.S. Patent Number US 11860914 B1), in view of Hamze (U.S. Patent Number US 20210303369 A1) and further in view of YOSHIKAWA (U.S. Patent Number US 20220391596 A1).
Regarding Claim 1, Qadrud-Din teaches a generation process of generating a prompt that corresponds to the work category which has been identified in the identification process; and an acquisition process of acquiring a generation sentence which has been generated based on the prompt. (see Qadrud-Din (2:20-42) “(17) Techniques and mechanisms described herein provide for the generation and querying of a database system based on natural language. A text generation interface system serves as an interface between one or more client machines and a text generation modeling system configured to implement a large language model. The text generation interface system may identify a set of documents including natural language, as well as one or more fields for querying the documents. The text generation interface system may then generate one or more prompts for extracting information related to the fields from the documents. The prompts may then be sent to the text generation modeling system, which may return structured text corresponding to the fields. The text generation interface system may then generate or update a database system based on the structured text. The database system may be queried to identify one or more documents based on search terms included in a search query. Natural text included in the identified documents may then be evaluated against the search query via one or more additional prompts completed by the text generation modeling system. The completed prompts may be used to prepare a comprehensive response to the search query.”)
Qadrud-Din does not specifically teach 1. An information processing apparatus, comprising at least one processor, the at least one processor carrying out: an identification process of identifying a work category of a one or more target sentences; However, Hamze does teach this limitation (See Hamze, [0076] “…voice-to-text functionality can also be available within a decision C-Task interface, so that the users can set the verbal agreement into writing.”) [0279] After the initiating resource 822 submits a final event completing the M-Task of generating the transaction, a smart terms engine of the dynamic modeling system 810 can automatically generate a T-Task for setting the smart terms of the transaction and assign the T-Task to the initiating resource 822 (step 802). For example, the T-Task can include providing prompts to a user of the dynamic modeling system 810 using an initiating smart assistant 812 for setting parameters for each of the smart terms of the transaction. The smart terms can include one or more of: terms and conditions, payment terms, warranties, trade terms, insurance terms, surveyor terms, digital terms, or credit terms. In some implementations, the smart terms engine generates a respective different T-Task corresponding to each category of smart terms, e.g., a first T-Task for setting the terms and conditions of the transaction, a second T-Task for setting the payment terms of the transaction, a third T-Task for setting the warranties of the transaction, a fourth T-Task for setting the credit terms of the transaction, etc. In some implementations, the smart terms can also identify how intellectual property associated with the transaction will be assigned to respective resources of the transaction.”)
Qadrud-Din and Hamze are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a generation process of generating a prompt that corresponds to the work category which has been identified in the identification process; and an acquisition process of acquiring a generation sentence which has been generated based on the prompt of Qadrud-Din to incorporate An information processing apparatus, comprising at least one processor, the at least one processor carrying out: an identification process of identifying a work category of a target of Hamze. This allows the execution of tasks and dynamic queries as recognized by Hamze [0014-0016].
Qadrud-Din in view of Hamze do not specifically teach wherein each of a plurality of work categories However YOSHIKAWA does teach this limitation. (see YOSHIKAWA [0018] “… summarizing a news article.”) is associated with one or more elements of a sentence by predefined association information, (see YOSHIKAWA [0018] “… the input sentence x obtains the probability distribution of word strings concerning the translation sentence”) wherein in the generation process, the at least one processor performs: determining, based on the predefined association information, the one or more elements corresponding to the work category identified in the identification process; (see YOSHIKAWA [0018] “For example, in summarizing a news article, inputting an original sentence into the language model M1 as the input sentence x obtains, as the output (y) of the language model M1, information (probability distribution of word strings P(y|x)) concerning the summary sentence.”) carrying out an extraction process of extracting, from the one or more target sentences, the determined one or more elements corresponding to the identified work category by using a pretrained work-category-specific model corresponding to the identified work category, (see YOSHIKAWA [0051]” Then, the dummy-context acquisition unit 31 generates a plurality of dummy contexts based on the output result (probability distribution of word strings) obtained by inputting the input sentence x into the constructed machine learning model (document generation model) (S11a). For example, the dummy-context acquisition unit 31 generates the dummy contexts by changing the combination of each word for which the probability value in the probability distribution is greater than a specific threshold value. The processing subsequent to S11a is performed in the same manner as that in S1.”) wherein the work-category-specific model is prepared for each work category; and (see YOSHIKAWA [0018] The natural language processing using the language model M1 may be any of summarizing news articles, responding in interactive systems, translating in translation systems, and the like.”) generating the prompt corresponding to the identified work category by applying content of an instruction sentence (see YOSHIKAWA [0029] “receives input of the input sentence”) and content of the extracted one or more elements to a predetermined prompt template, (see YOSHIKAWA [0029] outputs a processing result (for example, response sentence) for the input sentence x via the input/output unit 10.”) wherein the predetermined prompt template includes a tag indicating the instruction sentence and a tag indicating an input sentence, (see YOSHIKAWA [0040-0041] “ the response sentence based on the probability distribution (probability mass function) of the prediction label (y.sub.0) output …obtains the prediction label (y.sub.j)”) and wherein the generated prompt is configured by associating the content of the instruction sentence with the tag indicating the instruction sentence, and associating the content of the extracted one or more elements with the tag indicating the input sentence. (see YOSHIKAWA [0040] “The response acquisition unit 32 is a processing unit that obtains, based on the output result when the input sentence x is input into the language model M1, the response sentence for the input sentence x. Specifically, the response acquisition unit 32 inputs information concerning the input sentence x into the language model M1 constructed based on the language model parameters 23 and obtains the probability distribution concerning the word strings (line of words) corresponding to the response sentence from the language model M1. As one example, the response acquisition unit 32 inputs the input sentence x into the language model M1 and obtains a prediction label (y.sub.0) concerning each word and a probability mass function such as the following expression (3) indicating the distribution of the label probability. The response acquisition unit 32 obtains the response sentence based on the probability distribution (probability mass function) of the prediction label (y.sub.0) output from the language model M1 in such a manner.”)
Qadrud-Din in view of Hamze and YOSHIKAWA are in the same field of endeavor of signal processing, therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the apparatus of combination of Qadrud-Din and Hamze to incorporate wherein each of a plurality of work categories is associated with one or more elements of a sentence by predefined association information, wherein in the generation process, the at least one processor performs: determining, based on the predefined association information, the one or more elements corresponding to the work category identified in the identification process; carrying out an extraction process of extracting, from the one or more target sentences, the determined one or more elements corresponding to the identified work category by using a pretrained work-category-specific model corresponding to the identified work category, wherein the work-category-specific model is prepared for each work category; and generating the prompt corresponding to the identified work category by applying content of an instruction sentence and content of the extracted one or more elements to a predetermined prompt template, wherein the predetermined prompt template includes a tag indicating the instruction sentence and a tag indicating an input sentence, and wherein the generated prompt is configured by associating the content of the instruction sentence with the tag indicating the instruction sentence, and associating the content of the extracted one or more elements with the tag indicating the input sentence of YOSHIKAWA. This allows correct output for tasks as recognized by YOSHIKAWA [0003-0005].
As to Claim 2, Qadrud-Din in view of Hamze and further in view of YOSHIKAWA teaches 2. The information processing apparatus according to claim 1,
Furthermore, Qadrud-Din teaches wherein: in the generation process, the at least one processor generates the prompt which includes content corresponding to an instruction from a user. (see Qadrud-Din, “(10:25-33) “(66) A request from a client machine to generate a novel text portion is received at 402. In some embodiments, the request may include a query portion. The query portion may include natural language text, one or more instructions in a query language, user input in some other format, or some combination thereof. For instance, the query portion may include an instruction to “write an email”, “summarize documents”, or “research case law”.”)
As to Claim 3, Qadrud-Din in view of Hamze and further in view of YOSHIKAWA teaches 3. The information processing apparatus according to claim 2,
Furthermore, Qadrud-Din teaches wherein: the work category includes work to generate or edit a contract; and the prompt includes an instruction to generate or edit a contract. (see Qadrud-Din, (5:30-45) “(33) According to various embodiments, 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, performing legal research, preparing for a depositions, drafting legal interrogatories, drafting requests for admission, drafting requests for production, briefing a litigation case, responding to requests for admission, responding to interrogatories, responding to requests for production, analyzing cited authorities, and answering a complaint.”)
Regarding Claim 4, Qadrud-Din in view of Hamze and further in view of YOSHIKAWA teaches 4. The information processing apparatus according to claim 2,
Furthermore, Qadrud-Din teaches wherein: the work category includes work to check a contract; and the prompt includes an instruction to check a contract. (see Qadrud-Din (5:30-45) “(33) According to various embodiments, 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, performing legal research, preparing for a depositions, drafting legal interrogatories, drafting requests for admission, drafting requests for production, briefing a litigation case, responding to requests for admission, responding to interrogatories, responding to requests for production, analyzing cited authorities, and answering a complaint.”)
Regarding Claim 5, Qadrud-Din in view of Hamze and further in view of YOSHIKAWA teaches 5. The information processing apparatus according to claim 2,
Furthermore, Qadrud-Din teaches wherein: the at least one processor further carries out a revision process of revising the generation sentence in accordance with the work category which has been identified in the identification process. (See Qadrud-Din (6:15-35) “(37) An answer to the query is determined at 106 by evaluating the text of the subset of the documents based on the query. In some embodiments, the answer to the query may be determined based at least in part on an interaction with a text generation modeling system. For instance, the identified documents may be used to create one or more prompts to the text generation modeling system. A prompt may include a portion of text from the identified documents and instructions based at least in part on the query. The text generation modeling system may complete the prompt, and the text generation interface system may determine an overall answer to the query based on the response or responses provided by the text generation modeling system. Additional details regarding the answering of a query based on an evaluation of the text of a subset of documents are discussed with respect to the method 1500 shown in FIG. 15.”)
Regarding Claim 7, Qadrud-Din in view of Hamze and further in view of YOSHIKAWA teaches 7. The information processing apparatus according to claim 6,
Furthermore, Qadrud-Din teaches wherein: in the generation process, the at least one processor generates the prompt which includes (i) an instruction sentence corresponding to the instruction from the user and (ii) the one or more elements which have been extracted in the extraction process. (see Qadrud-Din, (10:25-33) “(66) A request from a client machine to generate a novel text portion is received at 402. In some embodiments, the request may include a query portion. The query portion may include natural language text, one or more instructions in a query language, user input in some other format, or some combination thereof. For instance, the query portion may include an instruction to “write an email”, “summarize documents”, or “research case law”.”) (see Qadrud-Din, (19:25-60) “(124) In some embodiments, determining the chat prompt at 806 may involve selecting a chat prompt template configured to instruct the text generation modeling system 270 to revise correspondence. For instance, the user input received at 802 may include a request to revise correspondence. The request may also include information such as the correspondence to be revised, the nature of the revisions requested, and the like. For instance, the request may include an indication that the tone of the letter should be changed, or that the letter should be altered to discuss one or more additional points. Then, the chat response message received at 816 may include novel text for including in the revised correspondence. The novel text may be parsed and incorporated into a revised correspondence letter, which may be included with the chat output message sent at 822 and presented to the user at 824. For instance, the parser may perform operations such as formatting the novel text in a letter format. (125) An example of a prompt template that may be used to generate a prompt for determining an aggregate of a set of summaries of documents is provided below: A lawyer has submitted the following question: $$QUESTION$$ {{question}} $$/QUESTION$$ We have already reviewed source documents and extracted references that may help answer the question. We have also grouped the references and provided a summary of each group as a “response”: $$RESPONSES$$ {% for response in model_responses %} {{loop.index}}. {{response}} {% endfor %} $$/RESPONSES$$”)
Regarding Claim 8, Qadrud-Din in view of Hamze and further in view of YOSHIKAWA teaches 8. The information processing apparatus according to claim 7,
Furthermore, Qadrud-Din teaches wherein: in the generation process, the at least one processor carries out the extraction process with use of a language model which has been trained for each work category. (see Qadrud-Din, (21:2-10) “(136) The one or more summarize prompts 908 are then sent to the text generation modeling system 270 via one or more summarize prompt messages 912. The text generation modeling system 270 generates one or more raw summaries at 914, which are then sent back to the text generation interface system 210 via one or more summarize response messages at 916.”)
Regarding Independent Claim 9, Claim 9 contains limitations similar to that of Claim 1 and is rejected under similar rationale. Furthermore Qadrud-Din teaches 9. An information processing method, comprising: identifying, by at least one processor, a work category corresponding to a target sentence; generating, by the at least one processor, (see Qadrud-Din, (16:15-45”) (109) FIG. 7 illustrates one example of a computing device 700, configured in accordance with one or more embodiments. According to various embodiments, a system 700 suitable for implementing embodiments described herein includes a processor 701, a memory module 703, a storage device 705, an interface 711, and a bus 715 (e.g., a PCI bus or other interconnection fabric.) System 700 may operate as variety of devices such as an application server, a database server, or any other device or service described herein. Although a particular configuration is described, a variety of alternative configurations are possible. The processor 701 may perform operations such as those described herein. Instructions for performing such operations may be embodied in the memory 703, on one or more non-transitory computer readable media, or on some other storage device. Various specially configured devices can also be used in place of or in addition to the processor 701. The interface 711 may be configured to send and receive data packets over a network. Examples of supported interfaces include, but are not limited to: Ethernet, fast Ethernet, Gigabit Ethernet, frame relay, cable, digital subscriber line (DSL), token ring, Asynchronous Transfer Mode (ATM), High-Speed Serial Interface (HSSI), and Fiber Distributed Data Interface (FDDI). These interfaces may include ports appropriate for communication with the appropriate media. They may also include an independent processor and/or volatile RAM. 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.”)
Regarding Independent Claim 10, Claim 10 contains limitations similar to that of Claim 1 and is rejected under similar rationale. Furthermore Qadrud-Din teaches 10. A non-transitory storage medium storing a program which causes a computer to carry out: (see Qadrud-Din, (16:15-45”) (109) FIG. 7 illustrates one example of a computing device 700, configured in accordance with one or more embodiments. According to various embodiments, a system 700 suitable for implementing embodiments described herein includes a processor 701, a memory module 703, a storage device 705, an interface 711, and a bus 715 (e.g., a PCI bus or other interconnection fabric.) System 700 may operate as variety of devices such as an application server, a database server, or any other device or service described herein. Although a particular configuration is described, a variety of alternative configurations are possible. The processor 701 may perform operations such as those described herein. Instructions for performing such operations may be embodied in the memory 703, on one or more non-transitory computer readable media, or on some other storage device. Various specially configured devices can also be used in place of or in addition to the processor 701. The interface 711 may be configured to send and receive data packets over a network. Examples of supported interfaces include, but are not limited to: Ethernet, fast Ethernet, Gigabit Ethernet, frame relay, cable, digital subscriber line (DSL), token ring, Asynchronous Transfer Mode (ATM), High-Speed Serial Interface (HSSI), and Fiber Distributed Data Interface (FDDI). These interfaces may include ports appropriate for communication with the appropriate media. They may also include an independent processor and/or volatile RAM. 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.”)
Regarding Claim 11, Qadrud-Din in view of Hamze and further in view of YOSHIKAWA teaches 11. (New) The information processing apparatus according to claim 1,
Furthermore, Qadrud-Din teaches wherein in the extraction process, the at least one processor automatically extracts the determined one or more elements by executing an industry-type-specific model which has been trained by machine learning specific to the identified work category. (see Qadrud-Din, (16:15-45”) “(169) At 1104, the text generation interface system identifies a skill associated with the request by transmitting a prompt to the text generation modeling system. The text generation modeling system returns a response identifying correspondence generation as the appropriate skill. Additional details regarding skill identification are discussed with respect to FIG. 8.”)
Regarding Claim 12, Qadrud-Din in view of Hamze and further in view of YOSHIKAWA teaches 12. (New) The information processing apparatus according to claim 1,
Furthermore, Qadrud-Din teaches wherein the acquisition process includes transmitting the generated prompt to a generation apparatus (see Qadrud-Din, (6:65-7:2) “(41)… the text generation modeling system 270 may be configured to receive, process, and respond to requests via the communication interface 272, which may be configured to facilitate communications via a network”)
that executes a large language model via a communication network, and receiving the generation sentence output from the large language model. (see Qadrud-Din, (31:5-11) “(199) …As one example, a prompt template may include a set of instructions for causing a large language model to identify values for the one or more data fields based on the one or more clauses. The prompt template may also include one or more additional instructions, such as an instruction to format the text generated by the text generation model as structured text.”)
Regarding Claim 13, Qadrud-Din in view of Hamze and further in view of YOSHIKAWA teaches 13. (New) The information processing apparatus according to claim 1,
Furthermore, Qadrud-Din teaches wherein the work category includes checking a contract, (see Qadrud-Din, (13:65-14:2) “(89)…domain-specific text chunking constraint may discourage division of a question and answer in a deposition transcript or other question/answer context. As another example, a domain-specific text chunking constraint may discourage splitting of a contract clause.”) wherein the predetermined prompt template further includes a point to be checked and a tag indicating the point, wherein the generated prompt is further configured by associating content of the point with the tag indicating the point to instruct outputting an itemized evaluation result, (see Qadrud-Din, (22:4-9) “(143)… Answer based on the full text, not just a portion of it. For each and every question, include verbatim quotes from the text (in quotation marks) in the answer. If the quote is altered in any way from the original text, use ellipsis, brackets, or [sic] for minor typos. Be exact in your answer. Check every letter.”) and wherein the at least one processor further carries out a revision process of revising the generation sentence based on the itemized evaluation result for the point. (see Qadrud-Din, (28:32-39) “(176) At 1134, the text generation interface system revises the correspondence by transmitting one or more prompts to the text generation modeling system. The requests may include the correspondence generated at 1122 as well as one or more results of the analysis of the factual claims. In this way, the text generation modeling system may revise the correspondence for accuracy, for instance by removing factual claims deemed to be inaccurate.”)
Conclusion
THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KRISTEN MICHELLE MASTERS/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659
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