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 .
Status of the Claims
Claims 1, 2, 4-12, 15, and 16 have been amended. Claim 14 has been canceled. Claims 1-13, 15, and 16 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 09/20/2024 was filed before the mailing of this action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: A System for Document Inference. This is mere suggestion and applicant may choose different title, so long as it is descriptive and indicative of the invention to which the claims are directed.
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 1-13, 15, and 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-12, 15, and 16 recite an apparatus (i.e. machine), claim 13 recites a method (i.e. process). Therefore claims 1-13, 15, and 16 fall within one of the four statutory categories of invention.
Independent claims 1 and 13 recite the limitations: an acquisition process that acquires [a target document] which is at least a part of a target draft written contract; an inference process that carries out inference related to [the target document], with use of [an inference model] trained by using [a written contract template] of a target company and [a concluded written contract] related to at least one of the target company and another company which differs from the target company; and a generation process that generates output information obtained with reference to an inference result obtained by the inference process. The invention and claims are drawn towards performing an inference related to a target document, and the claims recite limitations the correspond to certain methods of organizing human activity (managing personal interactions, relationships, behavior; business relations; commercial or legal interactions) as evidenced by limitations detailing acquiring a document a part of a target draft written contract, carrying out inference related to the document using a template of a target company, and generating output information obtained with reference to an inference result. The claims also recite limitations that correspond to mental processes (observation, evaluation, judgment, opinion), as evidenced by limitations detailing the observation of a target document and using an inference model to carry out inference by using a model trained on a template of a target company and concluded contract of the target company and another company. The claims recite an abstract idea.
Note: the features or elements in brackets in the above section are inserted for reading clarity, but are analyzed as “additional elements” in under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: an information processing apparatus comprising a processor, an inference model, a written contract template, a concluded written contract, and a target document. The additional elements of the information processing apparatus and processor and inference model are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, the inference model, written contract template, concluded written contract, and target document amounts to generally linking the judicial exception to a particular field of use. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Independent claim 12 recites the limitations: an acquisition process that acquires [a written contract template] of a target company and [a concluded written contract] related to at least one of the target company and another company which differs from the target company; and a training process that trains, with reference to information acquired by the acquisition process, [an inference model] that carries out inference on [a target document]. The invention and claims are drawn towards performing an inference related to a target document, and the claims recite limitations the correspond to certain methods of organizing human activity (managing personal interactions, relationships, behavior; business relations; commercial or legal interactions) as evidenced by limitations detailing [a written contract template] of a target company and [a concluded written contract] related to at least one of the target company and another company which differs from the target company, and carrying out an inference on a target document. The claims also recite limitations that correspond to mental processes (observation, evaluation, judgment, opinion), as evidenced by limitations detailing training with reference to information acquired by the acquisition process, [an inference model] that carries out inference on [a target document]. The claims recite an abstract idea.
Note: the features or elements in brackets in the above section are inserted for reading clarity, but are analyzed as “additional elements” in under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: an information processing apparatus comprising a processor, an inference model, a written contract template, a concluded written contract, and a target document. The additional elements of the information processing apparatus and processor and inference model are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, the inference model, written contract template, concluded written contract, and target document amounts to generally linking the judicial exception to a particular field of use. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is 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 integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Dependent claim 15 recites the limitation of a computer-readable non-transitory storage medium storing an information processing program for causing a computer to function as the information processing apparatus according to claim 1, the program for causing the computer to carry out the acquisition process, the inference process, and the generation process. The limitation is further directed to the abstract idea analyzed above. The claim also recites the additional elements of a computer-readable non-transitory storage medium and a computer. The additional elements amount to “apply it” or merely using a computer as a tool to implement the judicial exception. Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Further, when viewed as an ordered combination, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea. The claim is not patent eligible.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-7, 10, 12, 13, 15, and 16 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Boyce (2021/0157862).
Claim 1: An information processing apparatus comprising at least one processor, the at least one processer carrying out:
an acquisition process that acquires a target document which is at least a part of a target draft written contract; (Boyce ¶0044 disclosing the negotiation engine obtains preference information from first party and second party; the information may include setting information for a contract or other such document that relates to specific document sections (e.g., clauses or provisions) of a document; ¶0045 the sections of the document for which preference information is gathered may be selected by negotiation engine; ¶0046 sections of the document (“document sections”) for which preference information can constitute, in aggregate, a subset of the document)
an inference process that carries out inference related to the target document, with use of an inference model trained by using a written contract template of a target company and a concluded written contract related to at least one of the target company and another company which differs from the target company; and (Boyce ¶0051 for open-ended document sections, the parties may be presented with some pre-written options that represent settings commonly selected by parties to similar documents; ¶0061 once preference information 203 and 205 is obtained by negotiation engine a candidate document may be determined, including, for example, a proposed document or document summary; based on preference information, as well as additional information, negotiation engine can determine candidate document; ¶0062 additional information can include information associated with one or more documents, one or more third-party providers, etc., and may be obtained either before or after the preference information is obtained, in real-time during the operation of the negotiation engine; ¶0063 additional information can include historical preference data gathered, this can include preference information for documents of the same type as the current document, with different counterparties, or documents of a different type (with either the same or different counterparties), where the preference information gathered correlates with the sections of the current document; ¶0073 disclosing the engine can generate candidate document; candidate document can include appropriate text for the document sections based on at least preference information and as well as additional information from third-party providers; ¶0089 disclosing a set of documents 302 and 309 is obtained that can be used to train one or more models or neural networks to associate a section of a document with one or more other document sections; documents and sections can be obtained from a number of third-party providers of such documents; ¶0093 disclosing documents can be analyzed to determine which documents include data sufficient to identify a type of document and sections of the document, and those documents can be considered a training set to be used to train neural networks or other such models; ¶0094 the training documents are used as training data; various documents provided by third-party sources or generated from documents provided the third parties can be used for training; ¶0098 once the neural network is trained, the network can be used to, for example, associate a section of a document (a "document section") with one or more other document sections; see also ¶0101; ¶0104; ¶0157 neural network can thus be trained on the information to learn relationships between the preference information and documents in the training data so that when a trained model receives preference information for a type of document, the model can use the relationships it has learned to infer the candidate document that satisfies the preference information from the users)
a generation process that generates output information obtained with reference to an inference result obtained by the inference process. (Boyce ¶0107 when full training set has been obtained, using any appropriate criterion as discussed or suggested herein, then the training set generation can complete, and the documents can be stored for training and other purposes; ¶0114 inputs can be preference information as described herein and can be received at a negotiation engine configured to automatically generate or otherwise determine a contract or contract information that satisfies constraints of at least one party in a negotiation)
Claim 13: Claim 13 is directed to a method. Claim 13 recites limitations that are parallel in nature as those addressed above for claim 1, which is directed towards an apparatus. Claim 13 is therefore rejected for the same reasons as set forth above for claim 1.
Claim 2: The information processing apparatus according to claim 1, wherein:
in the acquisition process, the at least one processor acquires, as the target document, at least a part of a draft written contract between the target company and the another company; and (Boyce ¶0044 disclosing the negotiation engine obtains preference information from first party and second party; the information may include setting information for a contract or other such document that relates to specific document sections (e.g., clauses or provisions) of a document; ¶0045 the sections of the document for which preference information is gathered may be selected by negotiation engine; ¶0046 sections of the document (“document sections”) for which preference information can constitute, in aggregate, a subset of the document; ¶0051 the parties would typically be able to enter any text of their choosing, although the parties may also be presented with some pre-written options that represent settings commonly selected by parties to similar documents)
the inference model is a model trained at least with reference to information on past negotiation between the target company and the another company. (Boyce ¶0061 once preference information 203 and 205 is obtained by negotiation engine a candidate document may be determined, including, for example, a proposed document or document summary; based on preference information, as well as additional information, negotiation engine can determine candidate document; ¶0063 additional information can include historical preference data gathered, this can include preference information for documents of the same type as the current document, with different counterparties, or documents of a different type (with either the same or different counterparties), where the preference information gathered correlates with the sections of the current document; ¶0065 for example, historical information associated with a party, which can indicate previously set section settings for a party. In an example, the historical information can indicate that a party has requested and/or accepted a 5-year term on contracts of a particular type; ¶0089 a set of documents 302 and 309 is obtained that can be used to train one or more models or neural networks to associate a section of a document with one or more other document sections; ¶0093-¶0094; ¶0157 a model can be trained on preference information for particular document types and document settings for sections of the document types to select a candidate contract; e.g., historical data for preference information and documents generated or otherwise selected based on the preference information from one or more users can be used to train a model such as a neural network)
Claim 3: The information processing apparatus according to claim 2, wherein the inference model is a model trained at least with reference to information on past negotiation between the target company and one or more companies which are included in an industry to which the another company belongs. (Boyce ¶0051 the parties may be presented with some pre-written options that represent settings commonly selected by parties to similar documents; ¶0061; ¶0062; ¶0063 additional information (used to generate the candidate document) can include historical preference data gathered from first party 202 and/or second party 204; this can include preference information for documents of the same type as the current document; ¶0065 information can include historical information associated with a party, which can indicate previously set section settings for a party; e.g., the historical information can indicate that a party has requested and/or accepted a 5-year term on contracts of a particular type; ¶0067; ¶0068 disclosing additional information can include industry information; industry information can include, for example, document information as it pertains to a particular industry; e.g., in certain industries, it may be common for commercial leases to last for 24 months; ¶0089; ¶0090; ¶0092 an “actions” document section that includes the exchange of promises that is the subject matter of the agreement can be related to a “definitions” document section or other such section; ¶0093 documents can be analyzed to determine which documents include data sufficient to identify a type of document and sections of the document, and those documents can be considered a training set to be used to train neural networks or other such models; ¶0102 document section can be associated with document section 426and 416 because they relate to similar subject matter of a legal contract; ¶0134 the relative importance can be estimated through statistics of past contract acceptance; ¶0150; ¶0157 a model can be trained on preference information for particular document types and document settings for sections of the document types to select a candidate contract; e.g., historical data for preference information and documents generated or otherwise selected based on the preference information from one or more users can be used to train a model such as a neural network)
Claim 4: The information processing apparatus according to claim 2, wherein the inference model is a model trained at least with reference to a draft written contract that was not concluded in the past between the target company and the another company. (Boyce ¶0106 disclosing the in the training process, if it is determined that a document or document section does not exhibit the attribute for a particular category, the document can be excluded from the training set; ¶0134 disclosing relative importance can be estimated based on the designers' intuitive judgments if past contracts are not available)
Claim 5: The information processing apparatus according to claim 2, wherein the inference model is a model trained at least with reference to a difference between the written contract template of the target company and the concluded written contract. (Boyce ¶0095 disclosing the testing documents used to test the trained neural network; results can be analyzed and if the results are acceptable, such as where the accuracy at least meets a minimum accuracy threshold for some or all of the classifications; ¶0150 disclosing the stopping criterion with the training including the stopping threshold can be based on historical data of contracts shown to be satisfactory to users, where a satisfactory contract can be a contract accepted by users; the historical data can be analyzed to determine an average, median, or other such document selection score associated with accepted contracts; ¶0093 disclosing documents can be analyzed to determine which documents include data sufficient to identify a type of document and sections of the document, and those documents can be considered a training set to be used to train neural networks or other such models)
Claim 6: The information processing apparatus according to claim 2, wherein the inference model is a model trained at least with reference to the number of written contracts that were concluded in the past between the target company and the another company. (Boyce ¶0150 disclosing determining that a candidate document is associated with a document selection score less than a predetermined stopping threshold; the stopping threshold can be a predetermined number of candidate documents, where the predetermined number of candidate documents can be manually set as all possible candidate documents, half of all possible candidate documents, etc.; ¶0153 disclosing a process of determining the documents to analyze; a determination can be made whether the number of candidate documents is below a threshold number of candidate documents; where the number of candidate documents is below the threshold number of candidate documents, or otherwise satisfies the threshold number of candidate documents, the plurality of candidate documents can be analyzed; he next candidate document in a list of the plurality of candidate documents can be selected and processed until all or at least a threshold number of candidate documents are processed; ¶0157 a model can be trained on preference information for particular document types and document settings for sections of the document types to select a candidate contract; the neural network can thus be trained on the information to learn relationships between the preference information and documents in the training data so that when a trained model receives preference information for a type of document, the model can use the relationships it has learned to infer the candidate document that satisfies the preference information from the users)
Claim 7: The information processing apparatus according to claim 2, wherein the inference model is a model trained at least with reference to a history of negotiation between the target company and the another company. (Boyce ¶0063 additional information can include data useful to generate candidate document; additional information can include historical preference data gathered from first party 202 and/or second party 204; this can include preference information for documents of the same type as the current document, with different counterparties, or documents of a different type (with either the same or different counterparties), where the preference information gathered correlates with the sections of the current document; ¶0065 information can include, for example, historical information associated with a party, which can indicate previously set section settings for a party; negotiation engine can utilize that information when determining a candidate document; ¶0157 a model can be trained on preference information for particular document types and document settings for sections of the document types to select a candidate contract; e.g., historical data for preference information and documents generated or otherwise selected based on the preference information from one or more users can be used to train a model such as a neural network)
Claim 10: The information processing apparatus according to claim 1,wherein: the inference process includes inference related to a probability of concluding a contract with the another company; and in the generation process, the at least one processor generates output information that includes the probability of concluding the contract with the another company. (Boyce ¶0023 disclosing a ranking value or other such document selection value or score for a plurality of candidate contracts possible between the parties based on information from the parties, including their preferences and priority ranking for different sections (e.g., provisions) of the contract; the ranking value can be a combination or function of a set of document section ranking values or other similar values that quantify a likelihood of a setting for a candidate document section satisfying preference information associated with a party; ¶0024 contracts generated in accordance with approaches described herein will be more likely be acceptable to all the parties to the contract, and thus more likely to produce a successfully signed and executed contract; ¶0093 documents can be analyzed to determine which documents include data sufficient to identify a type of document and sections of the document, and those documents can be considered a training set to be used to train neural networks or other such models; ¶0134 disclosing the relative importance can be estimated through statistics of past contract acceptance; ¶0144-¶0146; ¶0150 the stopping threshold can be based on historical data of contracts shown to be satisfactory to users, where a satisfactory contract can be a contract accepted by users, e.g.,, the historical data can be analyzed to determine an average, median, or other such document selection score associated with accepted contracts)
Claim 12: An information processing apparatus comprising at least one processor, the at least one processer carrying out:
an acquisition process that acquires a written contract template of a target company and a concluded written contract related to at least one of the target company and another company which differs from the target company; and (Boyce ¶0044 disclosing the negotiation engine obtains preference information from first party and second party; the information may include setting information for a contract or other such document that relates to specific document sections (e.g., clauses or provisions) of a document; ¶0045 the sections of the document for which preference information is gathered may be selected by negotiation engine; ¶0046 sections of the document (“document sections”) for which preference information can constitute, in aggregate, a subset of the document; ¶0043; ¶0120 providers, components, etc., are illustrated as being separate entities and/or components; ¶0134 the first and second parties are different entities)
a training process that trains, with reference to information acquired by the acquisition process, an inference model that carries out inference on a target document. (Boyce ¶0051 for open-ended document sections, the parties may be presented with some pre-written options that represent settings commonly selected by parties to similar documents; ¶0061 once preference information 203 and 205 is obtained by negotiation engine a candidate document may be determined, including, for example, a proposed document or document summary; based on preference information, as well as additional information, negotiation engine can determine candidate document; ¶0062 additional information can include information associated with one or more documents, one or more third-party providers, etc., and may be obtained either before or after the preference information is obtained, in real-time during the operation of the negotiation engine; ¶0063 additional information can include historical preference data gathered, this can include preference information for documents of the same type as the current document, with different counterparties, or documents of a different type (with either the same or different counterparties), where the preference information gathered correlates with the sections of the current document; ¶0073 disclosing the engine can generate candidate document; candidate document can include appropriate text for the document sections based on at least preference information and as well as additional information from third-party providers; ¶0089 disclosing a set of documents 302 and 309 is obtained that can be used to train one or more models or neural networks to associate a section of a document with one or more other document sections; documents and sections can be obtained from a number of third-party providers of such documents; ¶0093 disclosing documents can be analyzed to determine which documents include data sufficient to identify a type of document and sections of the document, and those documents can be considered a training set to be used to train neural networks or other such models; ¶0094 the training documents are used as training data; various documents provided by third-party sources or generated from documents provided the third parties can be used for training; ¶0098 once the neural network is trained, the network can be used to, for example, associate a section of a document (a "document section") with one or more other document sections; see also ¶0101; ¶0104; ¶0157 neural network can thus be trained on the information to learn relationships between the preference information and documents in the training data so that when a trained model receives preference information for a type of document, the model can use the relationships it has learned to infer the candidate document that satisfies the preference information from the users)
Claim 15: A computer-readable non-transitory storage medium storing an information processing program for causing a computer to function as the information processing apparatus according to claim 1, the program for causing the computer to carry out the acquisition process, the inference process, and the generation process. (Boyce ¶0167 disclosing system may include storage subsystem including various computer-readable storage media, such as hard disk drives, solid-state drives (including RAM-based and/or flash-based SSDs), or other storage devices. In various embodiments, computer-readable storage media can be configured to store software, including programs, code, or other instructions; ¶0176 disclosing operating system that provides executable program instructions for the general administration and operation of that server and typically will include computer-readable medium storing instructions that, when executed by a processor of the server, allow the server to perform its intended functions; ¶0184 disclosing storage media and other non-transitory computer-readable media for containing code)
Claim 16: A computer-readable non-transitory storage medium storing an information processing program for causing a computer to function as the information processing apparatus according to claim 12,the program for causing the computer to carry out the acquisition process and the training process. (Boyce ¶0167 disclosing system may include storage subsystem including various computer-readable storage media, such as hard disk drives, solid-state drives (including RAM-based and/or flash-based SSDs), or other storage devices. In various embodiments, computer-readable storage media can be configured to store software, including programs, code, or other instructions; ¶0176 disclosing operating system that provides executable program instructions for the general administration and operation of that server and typically will include computer-readable medium storing instructions that, when executed by a processor of the server, allow the server to perform its intended functions; ¶0184 disclosing storage media and other non-transitory computer-readable media for containing code)
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 8 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boyce (2021/0157862) in view of Kawato (2022/0076363).
Claim 8: The information processing apparatus according to claim 2,
wherein the inference model is a model trained at least with reference to a history of a change in a written contract in the past between the target company and the another company.
Boyce discloses the inference model trained on past/historical contract information from the parties: (Boyce ¶0043 party 202 can represent the first party, and party 204 can represent the second party. The actual users of the system are authorized representatives of the two parties (if the parties are companies or other entities with multiple partners or employees) or maybe the parties themselves (if the parties are persons or single-party entities such sole proprietorships); ¶0063 additional information can include data useful to generate candidate document; additional information can include historical preference data gathered from first party 202 and/or second party 204; this can include preference information for documents of the same type as the current document, with different counterparties, or documents of a different type (with either the same or different counterparties), where the preference information gathered correlates with the sections of the current document; ¶0065 information can include, for example, historical information associated with a party, which can indicate previously set section settings for a party; ¶0157 a model can be trained on preference information for particular document types and document settings for sections of the document types to select a candidate contract; e.g., historical data for preference information and documents generated or otherwise selected based on the preference information from one or more users can be used to train a model such as a neural network). Boyce does not explicitly disclose the inference model is a model trained at least with reference to a history of a change in a written contract in the past between the target company and the another company. Kawato suggests or discloses this limitation/concept: (Kawato ¶0057 disclosing preparing a new version of a contract (modification); the combination of the contents added and deleted by the editing and the amendment and the added and deleted time series is referred to as an “amendment history”, and is information accompanying document information of the contract document; “amendment history” further includes a comment corresponding to the added or deleted contents and/or an identifier of the user edited or amended; the separation between versions may be at a timing when the user having edited or amended a version is replaced, or may be provided for each temporal group, and various modifications are conceivable; ¶0061 the version separation unit of the information processing apparatus generates and separates the past version contract documents 112b.sub.1 to 112b.sub.i including the contract document 111b being a template from the amendment history being the information accompanying the input contract document). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Boyce to include the inference model is a model trained at least with reference to a history of a change in a written contract in the past between the target company and the another company as taught by Kawato. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Boyce in order to reduce the workload in a series of modifications of a contract document (see ¶0007-¶0008 of Kawato).
Claim 9: The information processing apparatus according to 1, wherein:
the inference process includes inference related to correction of the target document; and in the generation process, the at least one processor generates output information that includes a proposal related to the correction of the target document.
Boyce discloses that the parties may correct portions of the target document: (Boyce ¶0050 the parties may be able to edit submitted settings or to submit or write another version of the section as their preferred setting). Boyce does not explicitly disclose that the inference process includes inference related to correction of the target document; and in the generation process, the at least one processor generates output information that includes a proposal related to the correction of the target document. Kawato suggests or discloses this limitation/concept: (Kawato ¶0068 the amendment candidate presentation unit presents the provision for proposal selected by the above series of operations together with the amendment candidate and the corresponding comment; ¶0072 since the input contract document is separated into versions based on the amendment history, the original template is specified, another contract document prepared from the same or similar template is specified, and among the contract documents, the amendment candidate is proposed from a version close in contents to the input contract document and the next version). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Boyce to include the inference process includes inference related to correction of the target document; and in the generation process, the at least one processor generates output information that includes a proposal related to the correction of the target document as taught by Kawato. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Boyce in order to reduce the workload in a series of modifications of a contract document (see ¶0007-¶0008 of Kawato).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Boyce (2021/0157862) in view of Wodetzki (2018/0268506).
Claim 11: The information processing apparatus according to claim 1, wherein:
the inference process includes inference related to future negotiation with the another company; and in the generation process, the at least one processor generates output information that includes a policy on negotiation with the another company.
Boyce discloses an inference process in generating contracts, but does not explicitly disclose that the inference process includes inference related to future negotiation with the another company; and in the generation process, the at least one processor generates output information that includes a policy on negotiation with the another company. Wodetzki suggests or discloses this limitation/concept: (Wodetzki ¶0059 gaining visibility into the contracting outcomes reflected in a party's contract portfolio enables that party to protect its business from dangerous contracts by providing users with information about how to improve future contract drafting and negotiation processes and outcome; insights gained through contract portfolio analysis can be used to “harvest” clauses from legacy contracts and automatically feed a clause library. Clauses can be classified and ranked for favorability and risk attributes, and made available to users drafting new agreements, supported by playbook guidance. Machine analysis of contractual outcomes can also be used to derive negotiation patterns, and to apply those patterns into templates and rule-sets for automated drafting. An example would be that analysis shows that all contracts of type A include a clause of type B. This inference is then used by the platform to propose a rule that all templates for new contracts of type A should include clause type B as a mandatory requirement. In one implementation, the template based drafting platform includes a set of drafting rules that are processed by assessing various input facts and using the template rules to generate a draft contract, without expert intervention, which includes clauses and attributes best suited to those facts). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Boyce to include that the inference process includes inference related to future negotiation with the another company; and in the generation process, the at least one processor generates output information that includes a policy on negotiation with the another company as taught by Wodetzki. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify Boyce in order to provide a system that uses machine evaluation of contract documents to quickly and inexpensively present contract portfolio data in an accurate, organized, searchable and time organized manner (see ¶0007 of Wodetzki).
Conclusion
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3628