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 .
DETAILED ACTION
Introduction
This office action is in response to Applicant’s submission filed on 12/23/2024. As such, claims 1-20 have been examined.
Claim Objections
Claims 6 and 12 are objected to because of the following informalities: A semicolon should be inserted after comprising. Appropriate correction is required.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a computer-readable recording medium that, under the broadest reasonable interpretation, claims limitations that cover performance of the limitations in the human mind with the assistance of physical aids (e.g., pen and paper), but for the recitation of generic or well-known or conventional computer components. That is, other than reciting “a non-transitory computer-readable recording medium and a computer” nothing in these claim limitations precludes the steps from practically being performed in the mind. As a whole, claim 1 pertains to grouping sentence that belongs to same category or topic, which is a mental process that a human can do. Individually, each of the limitations also pertains to a mental process and/or insignificant extra solution activity, for example:
acquiring a plurality of sentences that contain a plurality of words; (e.g., a human receiving sentences in printed paper.)
executing, on the plurality of sentences, processing of specifying sets of feature words from the plurality of words, based on sentence vectors of the sentences that contain the plurality of words and word vectors of the plurality of words; (e.g., the human sorting/breaking down the sentences into numbers and pick out the keywords.)
and classifying the plurality of sentences such that the sentences that have a same one of the sets of the feature words are included in a same one of clusters. (e.g., the human arranging sentence that contains similar meaning under the same category, this can be done using pen and paper and grouping similar meaning sentence under same topic in a notebook.)
The judicial exception is not integrated into a practical application. In particular, the claims only recites generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving, determining, or outputting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, 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.
Claim 1 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 limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 1 is not patent eligible.
The examiner further notes that the use of claimed generic computer components (“a non-transitory computer-readable recording medium and a computer”) to obtain, extract, and/or generate data invokes such generic computer components “merely as a tool to perform an existing process”. MPEP 2106.05(f). MPEP 2106.05(f) further explains:
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 1 recites generic computer components (“a non-transitory computer-readable recording medium and a computer”), with respect to performing tasks. MPEP 2106.05(d) and (f) further provides examples of court decisions where the courts found generic computing components to be mere instructions to apply a judicial exception, and further explains “increased speed” (e.g., using a computer to increase the speed of an otherwise mental process) does not provide an inventive concept. For example:
A commonplace business method or mathematical algorithm being applied on a general purpose computer, Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
A process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general-purpose computer, FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) (emphasis added).
Performing repetitive calculations. Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims.")
Claim 7 recites a method claim that corresponds to the CRM of claim 1 and is therefore rejected under the same grounds as claim 1 above. Claim 7 is not patent eligible.
Claim 13 recites an information processing device claim that corresponds to the CRM of claim 1 and is therefore rejected under the same grounds as claim 1 above. While claim 13 further recites “a memory, and a processor”, these are merely generic computer components recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component. Therefore, none of these limitations (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception, because in either case the additional limitations merely utilize generic computer components that amounts to no more than mere instructions to apply the exception using generic computer function. Claim 13 is not patent eligible.
Claims 2-6, 8-12 and 14-18 depend from independent claims 1, 7 and 13 respectively, do not remedy any of the deficiencies of claims 1, 7 and 13, and therefore are rejected on the same grounds as claims 1, 7 and 13 from above.
Claim 2 further comprising: wherein the processing of specifying the sets of feature words includes calculating cosine similarity between the sentence vectors of the sentences and the word vectors of the plurality of words, and specifying the words that have the word vectors of which the cosine similarity with the sentence vectors is equal to or greater than a threshold value, as the feature words. (e.g., the looking for similarity between words of the sentence to determine meanings of the sentence and determine which words are important.)
Claim 3 further recite: wherein the plurality of sentences is registered in a storage device, and the computer is further caused to execute the processing that includes generating inverted index information in which identification information that identifies the clusters to which the sentences belong is associated with position information on the sentences in the storage device, based on a classification result of the classifying. (e.g., sorting sentences into groups and indexing them, the human can record index in a notebook, which sentence is recorded in which page under which category.) [storage device is a generic computer component just to keep data]
Claim 4 further comprising: when search sentences that contain the plurality of words are received, specifying the sets of feature words from the plurality of words in the search sentences, based on the sentence vectors of the search sentences and the word vectors of the words in the search sentences; and locating the sentences that correspond to the search sentences from the storage device in a search, based on the identification information on the clusters for the specified sets of feature words and the inverted index information. (e.g., matching query to retrieval results, the human can determine the intent of the query and locate relevant sentence or text based on index information, looking up index to find the relevant sentence.)
Claim 5 further recites: when a plurality of the search sentences is received, specifying a plurality of feature sentences, based on the sentence vectors of the plurality of the search sentences; specifying the sets of feature words from each of the plurality of feature sentences; specifying the identification information on the clusters of the respective feature sentences, based on the sets of feature words that correspond to the feature sentences; and locating, in the search, a common sentence that corresponds to each of the feature sentences from the storage device, based on the identification information on the clusters of the respective feature sentences and the inverted index information. (e.g., receiving two search query sentence, determine the important words from the sentences, locating a common sentence that corresponds to the two search query sentences, basically look for what the two query sentences have in common, and find them in the index.)
Claim 6 further recites: increasing or decreasing a number of the plurality of feature sentences specified from the plurality of the search sentences. (e.g., query expansion or reduction.)
The analysis of Claims 8-12 corresponds to claims 2-6, and therefore similar rationale of rejection is applied to these claims respectively.
The analysis of Claim 14-18 corresponds to claim 2-6, and therefore similar rationale of rejection is applied to the claim.
In sum, claims 2-6, 8-12 and 14-18 depend from claims 1, 7 and 13 respectively, and further recite mental processes as explained above. None of the additional limitations recited in claims 2-6, 8-12 and 14-18 amount to anything more than the same or a similar abstract idea as recited in claims 1, 7 and 13. Nor do any limitations in claims 2-6, 8-12 and 14-18: (a) integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or (b) amount to significantly more than the judicial exception because the additional limitations of using generic computer components amounts to no more than mere instructions to apply the exception using generic computer components. Claims 2-6, 8-12 and 14-18 are 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 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) 1-2, 7-8 and 13-14 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Lopez (US 20230297784).
Regarding Claim 1, Lopez discloses: 1. A non-transitory computer-readable recording medium ([0085] The exemplary embodiments of the present inventive concept may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present inventive concept.) storing an information processing program for causing a computer to execute processing comprising: acquiring a plurality of sentences that contain a plurality of words; ([0005] According to an exemplary embodiment of the present inventive concept, a method for automated decision modelling from text is provided including obtaining a text corpus including a policy. Terms and syntax are identified within the text corpus related to the policy. Sentence similarities and co-references based on the terms and syntax are identified.)
executing, on the plurality of sentences, processing of specifying sets of feature words from the plurality of words, based on sentence vectors of the sentences that contain the plurality of words and word vectors of the plurality of words; ([0041] Sentence similarities may be determined based upon predetermined thresholds of matching identified terms, synonyms, semantics, syntax, and/or ontology. Anaphora resolution (AR) may be used to identify synonyms to identified terms and pronouns. In an embodiment, sentence fragments may be given text embedding vectors using pretrained language models (BART, BERT). A bag of noun phrases and verb phrases may be extracted from the fragments of text using an abstract meaning representation (AMR) parser. Similarity between any pair of text fragments may be calculated as the aggregated similarity of the text embedding vectors and the bags of noun-phrases of verb-phares (e.g., using S-Bert and cosine similarity between sentence embeddings to identify similar decisions/rules).) [Specifying sets of feature words is interpreted as extracting text embeddings and breaking down sentences into bags of noun/verb phrases. Using S-Bert to generate sentence embeddings reads on using “sentence vectors.”. Text embeddings and bags of noun/verb phrases conceptually represent individual words and their combinations, thereby reading on “word vectors.”]
and classifying the plurality of sentences such that the sentences that have a same one of the sets of the feature words are included in a same one of clusters. ([0047] In an embodiment, the decision model template components may be adjusted by the user (e.g., via the decision modelling from text client 122) and used to develop training sets for machine learning. For example, the machine learning may include identified terms, annotations, and/or semantic meanings of sentences (topics of sentence groupings, policy implementer, policy subject, decisions, rules, evidence, etc) from the text corpus. The decision modelling from text program 134 may use a cluster algorithm to group text spans based on embeddings. Using the cluster algorithm, text spans may be grouped within template fields according to identified categories and/or mutual annotations. Text spans may include sentences that are consecutive or non-consecutive, similar or dissimilar groups, from the same or different text corpus, singular or plural, and/or partial or complete.) Also see para 0045, similar sentence groupings and organized categories.
Regarding claim 2, Lopez discloses: all the element of claim 1,
Lopez further discloses: wherein the processing of specifying the sets of feature words includes calculating cosine similarity between the sentence vectors of the sentences and the word vectors of the plurality of words, and specifying the words that have the word vectors of which the cosine similarity with the sentence vectors is equal to or greater than a threshold value, as the feature words. ([0041] Sentence similarities may be determined based upon predetermined thresholds of matching identified terms, synonyms, semantics, syntax, and/or ontology. Anaphora resolution (AR) may be used to identify synonyms to identified terms and pronouns. In an embodiment, sentence fragments may be given text embedding vectors using pretrained language models (BART, BERT). A bag of noun phrases and verb phrases may be extracted from the fragments of text using an abstract meaning representation (AMR) parser. Similarity between any pair of text fragments may be calculated as the aggregated similarity of the text embedding vectors and the bags of noun-phrases of verb-phares (e.g., using S-Bert and cosine similarity between sentence embeddings to identify similar decisions/rules).)
Regarding Claim 7, it is a method claim that recite similar elements from claim 1, therefor the rationale applied in the rejection of claim 1 is also applicable.
Regarding Claim 8, it is a method claim that recite similar elements from claim 2, therefor the rationale applied in the rejection of claim 2 is also applicable.
Regarding Claim 13, Lopez discloses: 13. An information processing device comprising: a memory; and a processor coupled to the memory and configured to: ([0005] According to an exemplary embodiment of the present inventive concept, a computer system is provided for automated decision modelling from text. The system includes one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors.)
As for the rest of the claim, they claim the elements corresponding to claim 1, therefore the rationale applied in rejection of claim 1 is equally applicable.
Regarding Claim 14, it is an information processing device claim that recite similar elements from claim 2, therefor the rationale applied in the rejection of claim 2 is also applicable.
Claim Rejections - 35 USC § 103
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 3-5, 9-11, and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Lopez (US 20230297784), in view of Kumaran (US 20190102400).
Regarding claim 3, Lopez discloses: all the element of claim 1,
Lopez does not appear to disclose inverted index.
Kumaran in the related art discloses: wherein the plurality of sentences is registered in a storage device, and the computer is further caused to execute the processing that includes generating inverted index information in which identification information that identifies the clusters to which the sentences belong is associated with position information on the sentences in the storage device, based on a classification result of the classifying. ([0014] The subject technology provides a search index, which may be referred to as an interpretation index, that maps respective clusters of semantically similar phrases to documents that include at least one occurrence of any one of the phrases of the respective cluster. For example, a phrase and any semantically similar phrases may be grouped in a cluster that is assigned a cluster identifier. The search index maps each cluster identifier to document identifiers corresponding to documents that include at least one occurrence of any one of the phrases of the corresponding cluster. The subject technology may also provide a shared memory structure that maps each phrase to the cluster identifier(s) of the cluster(s) that the phrase belongs to. In this manner, the cluster identifiers mapped to a given phrase can be quickly located within the search index, and documents corresponding to the cluster identifiers may be quickly identified.) Also see para 0015.
Lopez and Kumaran are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lopez to combine the teaching of Kumaran, because an inverted search index may map search phrases to documents that contain the phrases. Thus, when a search query is received, the documents that contain the specific phrases of the query can be quickly identified (Kendrick, [0002]).
Regarding claim 4, Lopez and Kumaran disclose: all the element of claim 3,
Kumaran further discloses: for further causing the computer to execute the processing comprising: when search sentences that contain the plurality of words are received, specifying the sets of feature words from the plurality of words in the search sentences, based on the sentence vectors of the search sentences and the word vectors of the words in the search sentences; ([0014] The subject technology provides a search index, which may be referred to as an interpretation index, that maps respective clusters of semantically similar phrases to documents that include at least one occurrence of any one of the phrases of the respective cluster. For example, a phrase and any semantically similar phrases may be grouped in a cluster that is assigned a cluster identifier. The search index maps each cluster identifier to document identifiers corresponding to documents that include at least one occurrence of any one of the phrases of the corresponding cluster. The subject technology may also provide a shared memory structure that maps each phrase to the cluster identifier(s) of the cluster(s) that the phrase belongs to. In this manner, the cluster identifiers mapped to a given phrase can be quickly located within the search index, and documents corresponding to the cluster identifiers may be quickly identified.) Also see para 0015.
and locating the sentences that correspond to the search sentences from the storage device in a search, based on the identification information on the clusters for the specified sets of feature words and the inverted index information. ([0014] The subject technology provides a search index, which may be referred to as an interpretation index, that maps respective clusters of semantically similar phrases to documents that include at least one occurrence of any one of the phrases of the respective cluster. For example, a phrase and any semantically similar phrases may be grouped in a cluster that is assigned a cluster identifier. The search index maps each cluster identifier to document identifiers corresponding to documents that include at least one occurrence of any one of the phrases of the corresponding cluster. The subject technology may also provide a shared memory structure that maps each phrase to the cluster identifier(s) of the cluster(s) that the phrase belongs to. In this manner, the cluster identifiers mapped to a given phrase can be quickly located within the search index, and documents corresponding to the cluster identifiers may be quickly identified.)
Where the rationale for the combination would be similar to the one already provided.
Regarding claim 5, Lopez and Kumaran disclose: all the element of claim 4,
Kumaran further discloses: when a plurality of the search sentences is received, specifying a plurality of feature sentences, based on the sentence vectors of the plurality of the search sentences; ([0014] The subject technology provides a search index, which may be referred to as an interpretation index, that maps respective clusters of semantically similar phrases to documents that include at least one occurrence of any one of the phrases of the respective cluster. For example, a phrase and any semantically similar phrases may be grouped in a cluster that is assigned a cluster identifier.)
specifying the sets of feature words from each of the plurality of feature sentences; ([0014] The subject technology provides a search index, which may be referred to as an interpretation index, that maps respective clusters of semantically similar phrases to documents that include at least one occurrence of any one of the phrases of the respective cluster. For example, a phrase and any semantically similar phrases may be grouped in a cluster that is assigned a cluster identifier.)
specifying the identification information on the clusters of the respective feature sentences, based on the sets of feature words that correspond to the feature sentences; ([0014] The subject technology provides a search index, which may be referred to as an interpretation index, that maps respective clusters of semantically similar phrases to documents that include at least one occurrence of any one of the phrases of the respective cluster. For example, a phrase and any semantically similar phrases may be grouped in a cluster that is assigned a cluster identifier.)
and locating, in the search, a common sentence that corresponds to each of the feature sentences from the storage device, based on the identification information on the clusters of the respective feature sentences and the inverted index information. ([0014] The subject technology may also provide a shared memory structure that maps each phrase to the cluster identifier(s) of the cluster(s) that the phrase belongs to. In this manner, the cluster identifiers mapped to a given phrase can be quickly located within the search index, and documents corresponding to the cluster identifiers may be quickly identified.)
Where the rationale for the combination would be similar to the one already provided.
Regarding Claims 9-11, are method claims that recite similar elements from claims 3-5, therefor the rationale applied in the rejection of claims 3-5 are also applicable.
Regarding Claims 15-17, are information processing device claims that recite similar elements from claims 3-5, therefor the rationale applied in the rejection of claims 3-5 are also applicable.
Claims 6, 12 and 18 and are rejected under 35 U.S.C. 103 as being unpatentable over Lopez (US 20230297784), in view of Kumaran (US 20190102400), and further in view of Kataoka (US 20210191939).
Regarding claim 6, Lopez and Kumaran disclose: all the element of claim 5,
Lopez and Kumaran do not appear to disclose adjusting number of feature sentences.
Kataoka in the related art discloses: for further causing the computer to execute the processing comprising increasing or decreasing a number of the plurality of feature sentences specified from the plurality of the search sentences. ([0074] When the question sentence of the user is composed of a large number of sentences (for example, three sentences), the FAQ data 150 wanted by the user may be specified. However, when the number of sentences which compose the question sentence is small, the number of hits of FAQ candidates increases rapidly, which makes the specification difficult. Therefore, the information processing device 1 according to this embodiment determines whether the number of sentences in the question sentence acquired from the user is three or larger, for example, and specifies the FAQ data 150 wanted by the user when the case is three sentences or larger, and notifies the user of a question supply request sentence to additionally acquire the question sentence when the case is smaller than three sentences.) Also see para 0037, 0077-0079. [The device varies its operational processing (either specifying FAQ data or asking for more sentences) based on the volume of sentences in the user's input, adjusting how the query is broken down and processed.]
Lopez, Kumaran and Kataoka are considered analogous art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Lopez and Kumaran to combine the teaching of Kataoka, because in the FAQ search, many candidates are often hit for a question sentence from a user. In this case, it is preferable that correct answer candidates are included in top few hits when search results are arranged in descending order of a degree of similarity (Kataoka, [0004]).
Regarding Claim 12, it is a method claim that recite similar elements from claim 6, therefor the rationale applied in the rejection of claim 6 is also applicable.
Regarding Claim 18, it is an information process device claim that recite similar elements from claim 6, therefor the rationale applied in the rejection of claim 6 is also applicable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Shen (US 20200193217) – discloses method/system for determining similarity between sentences. See Abstract, and figs. 1 and 3 for additional details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip H Lam whose telephone number is (571)272-1721. The examiner can normally be reached 9 AM-3 PM Pacific time.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHILIP H LAM/ Examiner, Art Unit 2656
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