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 .
The instant office action having application number 19/207,740, filed on May 14, 2025, has claims 1-8 pending in this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/14/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 8 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 8 purports to depend from claim 1; however, claim 8 does not further limit the subject matter of claim 1 because it fails to incorporate all of the limitations of the base claim. Specifically, claim 8 recites additional elements that are inconsistent with, or do not include, the limitations required by claim 1. Therefore, claim 8 is not a proper dependent claim as required by 35 U.S.C. § 112(d), which states that a dependent claim must contain a reference to a previous claim and specify a further limitation of the subject matter claimed. Accordingly, claim 8 is indefinite and must be amended to properly depend from the claim it references and to include all limitations of the parent claim.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-8 are rejected under 35 USC 101 because the claimed invention is directed to patent-ineligible subject matter. The claim recites an abstract idea implemented on a generic computer without additional elements that amount to significantly more.
Step 2A, Prong One: The Claim Recites an Abstract Idea
Claim 1 recites “An information processing method that is executed by a computer, the information processing method comprising: a) acquiring input text; b) acquiring an abstracted similar document based on a document registered in a document database, the abstracted similar document being similar to the input text and including confidential information abstracted by abstraction processing; and c) acquiring output text by using a text generation model, the output text being answer text for the input text when the input text and the abstracted similar document have been input, the text generation model being trained to generate answer text based on text and external information associated with the text.”. These limitations describe data analysis and mathematical manipulation, which are abstract ideas. Claim 1 recites an abstract idea because it includes acquiring input text, retrieving a similar document, and generating output text, which constitute data collection and analysis.
Step 2A, Prong Two: Not Integrated into a Practical Application
The claim merely applies the abstract idea using generic computer components such as a processor, database table, and hash table. The claim does not improve computer functionality or database technology, and therefore does not integrate the abstract idea into a practical application. . Claim 1 is not integrated into a practical application because it merely uses generic computer components to process information and generate an answer, without reciting a specific technological improvement or unconventional implementation.
Step 2B: No Inventive Concept
The additional elements recited are conventional database operations such as storing tables, retrieving values, computing hash values, and probing a hash table. These activities are well-understood, routine, and conventional. Thus, the claim does not provide an inventive concept.
Accordingly, Claim 1 is directed to an abstract idea under Step 2A, Prong One and is not integrated into a practical application under Step 2A, Prong Two.
Claims 2-7 depend on claim 1 and include all the limitations of this claim. Therefore, these claims are directed to the same abstract idea and the analysis must proceed to (Step 2A, Prong 2).
Claim 8 is rejected based on the same rationale as claim 1 above.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 8 recites “A recording medium having records thereon a computer-readable program …” It appears that the medium recited in the claims are not described in the specification as including a non-transitory tangible medium in a manner which enables it to act as a computer component to realize the computer program’s functionality. (In the Applicant’s specification, paragraph [0030], recites “The computer program P may be recorded on a non-transitory recording medium). Therefore, when the claims are interpreted broadly as transmission medium or signal, the claims appear to be non-statutory because they are not tangibly embodied in a manner so as to be executable. Applicant should add the “non-transitory" to the preamble.
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 1-6 and 8 are rejected under 35 USC 103(a) as being unpatentable over Qin et al. (US 2024/0346256 A1) (hereinafter Qin) in view of Cuomo et al. (US 2025/0086310 A1) (hereinafter Cuomo).
As per claims 1 and 8, Qin discloses a) acquiring input text [step 302, a query is received, paragraph 49]; b) acquiring an abstracted similar document based on a document registered in a document database, the abstracted similar document being similar to the input text and including confidential information abstracted by abstraction processing [In step 304, a first feature vector is generated. For instance, encoder 204 may generate first feature vector 226 that represents the meaning of query text string 220, paragraphs 45-50]; and c) acquiring output text by using a text generation model, the output text being answer text for the input text when the input text and the abstracted similar document have been input [GUI manager 108 may receive from response generator 110 a response 238 generated by LLM 214. As discussed above, LLM 214 may process augmented prompt 236 to generate a response 238 based on contextual information 215 using augmentation information 232, paragraph 54]. However Qin does not disclose the text generation model being trained to generate answer text based on text and external information associated with the text. On the other hand, Cuomo discloses the text generation model being trained to generate answer text based on text and external information associated with the text [The normalized statement “User inquiring about New York weather” is generated by the large language model privacy preservation system 150. The large language model privacy preservation system 150 stores the category and the normalized prompt data in the datastore 230, paragraph 48]. Qin teaches retrieval-augmented generation in which an input query is received, similar documents or augmentation information are retrieved from a stored document corpus based on semantic similarity, and both the query and retrieved information are provided as inputs to a text generation model to produce an answer response. Cuomo teaches privacy-preserving processing of text for language model systems, including abstracting or removing sensitive and confidential information from retrieved content before supplying it to a language model. Cuomo further teaches generating a normalized representation that retains key semantic elements while preventing disclosure of confidential data. It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Qin’s retrieval-augmented generation system by incorporating Cuomo’s abstraction processing on the retrieved similar documents. The motivation arises from the well-known problem that retrieval-augmented language models may inadvertently expose confidential or personally identifiable information contained in enterprise document repositories. Applying Cuomo’s abstraction step to Qin’s retrieved documents would predictably improve privacy and security while maintaining response accuracy, thereby yielding the claimed abstracted similar document.
As per claim 2, Qin discloses wherein the operation b) includes: b 11) acquiring a similar document by searching the document database for a document similar to the input text [retriever 210 may analyze indication(s) 230 received from comparator 208 to identify and retrieve augmentation information 232 from dataset(s) 112. For example, retriever 210 may identify and retrieve augmentation information 232 that correspond to the second feature vectors having a cosine similarity to the first feature vector that satisfies a first predetermined relationship with a first predetermined threshold, paragraph 52]; and b12) generating the abstracted similar document by abstracting the confidential information included in the similar document [satisfies a first predetermined relationship with a first predetermined threshold, that correspond to a first predetermined number of second feature vectors having the highest cosine similarities to the first feature vector, and/or that correspond to a second predetermined number of second feature vectors having the highest cosine similarities to the first feature vector that satisfy a second predetermined relationship with a second predetermined threshold, paragraph 52].
As per claim 3, Cuomo discloses wherein the operation b 11) includes: generating abstracted input text by abstracting a word included in the input text [The category can be a code, word, or phrase that is representative of the theme or topic of the prompt data 206, paragraph 65]; and acquiring the similar document by searching the document database for a document similar to the abstracted input text [the privacy preservation module 306 uses probabilities that are estimated and provided by the topic modeling technique to determine the category that corresponds to the topics, paragraph 65].
As per claim 4,Qin discloses wherein the operation b) includes: b21) abstracting confidential information included in each of a plurality of documents registered in the document database [a concatenation of user contextual information is encoded into a low-dimensional dense vector. For instance, encoder 204 may encode query text string 220 into first feature vector, paragraph 56]; and b22) acquiring the abstracted similar document by retrieving a document similar to the input text from among abstracted documents abstracted in the operation b21) [FIG. 5 depicts a flowchart 500 of a process for determining cosine similarities between a first feature vector and a plurality of second feature vectors, paragraph 59].
As per claim 5, Cuomo discloses wherein the operation c) includes inputting a document retrieved from among the abstracted documents in the operation b22) as the abstracted similar document to the text generation model [the large language model privacy preservation system 150 receives the prompt data 206 “How's the weather in New York?” The statement “How weather New York” is generated through pre-processing and the topic “Weather Information” is identified through topic modeling techniques applied by the large language model privacy preservation system 150. The large language model privacy preservation system 150 determines that “Weather Information” corresponds to the category code W001. The normalized statement “User inquiring about New York weather” is generated by the large language model privacy preservation system 150., paragraph 48].
As per claim 6, Qin discloses d) generating abstracted output text by abstracting confidential information included in the output text acquired in the operation c) [Encoder 204 may include one or more encoders that generate feature vectors based on a text string. For instance, encoder 204 may process query text string 220 to generate a first feature vector 226 that represents the meaning of query text string 220. Encoder 204 may provide first feature vector 226 to comparator 208. In embodiments, encoder 204 may also process augmentation information text string 222 to generate a second feature vector 224 that represents the meaning of augmentation information text string 222, paragraph 41].
Claim 7 is rejected under 35 USC 103(a) as being unpatentable over Qin et al. (US 2024/0346256 A1) (hereinafter Qin) in view of Cuomo et al. (US 2025/0086310 A1) (hereinafter Cuomo) and further in view of Brodie et al. (US 2009/0055887 A1) (hereinafter Brodie).
As per claim 7, the rejection of claim 7 is incorporated by claim 1 above. However the combination of references cited does not tech or suggest, wherein the abstraction processing in the operation b) includes processing for, by using ontology information that defines a hierarchical relationship of a plurality of concepts, abstracting the confidential information to a concept corresponding to a conceptual hierarchy level set in advance. On the other hand, Brodie discloses wherein the abstraction processing in the operation b) includes processing for, by using ontology information that defines a hierarchical relationship of a plurality of concepts, abstracting the confidential information to a concept corresponding to a conceptual hierarchy level set in advance [a privacy ontology may include a hierarchical organization of all of the types of data categories of PII and a rationale for the relationships between the levels and categories of information, paragraph 22, it is necessary to be able to de-identify PII data to an appropriate level of abstraction so that the medical research can proceed and yet the identities of those persons providing PII is protected, paragraph 25]. It would have been obvious to a person of ordinary skill in the art at the time of the invention to modify the confidential information abstraction processing of Qin in view of Cuomo, and further in view of Brodie, to include ontology-based hierarchical abstraction of confidential information. Qin teaches processing confidential information by performing abstraction and transformation operations in order to prevent disclosure of sensitive content while maintaining usability of the processed data. Cuomo further teaches applying structured semantic processing techniques, including concept-based abstraction, to generalize sensitive information in a controlled manner. Brodie explicitly teaches using ontology information defining hierarchical relationships among a plurality of concepts (privacy ontology) and abstracting personally identifiable or confidential information to an appropriate level of abstraction within that hierarchy (see paragraph [0022] and paragraph [0025]). A person of ordinary skill in the art would have been motivated to incorporate the ontology-based hierarchical abstraction taught by Brodie into the abstraction processing of Qin as enhanced by Cuomo because doing so would predictably improve privacy-preserving processing by enabling confidential information to be abstracted to a predefined conceptual hierarchy level, thereby providing more consistent and semantically meaningful de-identification.
Such a combination represents the use of known techniques for organizing and generalizing sensitive information (ontology-based hierarchical abstraction) applied to a known system for confidential information abstraction, yielding predictable results.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOOSHA ARJOMANDI whose telephone number is (571)272-9784. The examiner can normally be reached on (571)272-9784.
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February 7, 2026
/NOOSHA ARJOMANDI/Primary Examiner, Art Unit 2166