DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to the submission filed February 11, 2026. Claims 1-20 are pending.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1, 8 and 15 are directed to a method, system and non-transitory medium for generating text. The claims recite features to
retrieve, from a database, custom text data related to a client at a client device, wherein the custom text data comprises memo data, transaction data, loan data, or asset data related to the client is a data gathering step that can be achieved by the person accessing and reviewing a database of various data;
generate instructions to prompt a large language model to generate text data, wherein the instructions are configured to prompt the large language model to extract data from the custom text data for the generation of the text data can be achieved by the person, using pen and paper, listing a desired search/request query; and
generate, using the large language model, the text data, wherein the generated text data is generated based on the custom text data and the instructions can be achieved by the person, using natural language processing rules and principles applied to the custom data, generating/determine responses that satisfy the query.
The recited limitations are directed a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of the generic computing device, processor, memory, programming instructions and generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recited generic computing device, processor, memory, programming instructions and generic computer components amounts to no more than mere instructions to apply the exception using generic computer components. Accordingly, the 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 are not patent eligible.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as indicated with respect to integration of the abstract idea into a practical application, the additional elements of the generic computing device, processor, memory, programming instructions and generic computer components to perform the various steps amounts 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. The claims are not patent eligible.
Dependent claims 2-7, 9-14, and 16-20 do not integrate the judicial exception into a practical application and do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations of the dependent claims are directed to steps of organizing or manipulating data, utilizing natural language processing rules and principles to process text for extracting and categorizing data; process social/marketing schemes; processing the data to select preferred information; and providing desired data/information to clients/users.
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.
Claims 1-4, 8-11, and 15-18 are rejected under 35 U.S.C. 102 (a)(1)/(a)(2) as being anticipated by Padmashali et al (US Patent Application Publication No. 2025/0148020), hereinafter Padmashali.
Regarding claim 1, Padmashali teaches a method comprising:
retrieving, by a computing device from a database, custom text data related to a client at a client device, wherein the custom text data comprises memo data, transaction data, loan data, or asset data related to the client [para 0070 – database 208 that stores original documents; para 0047-0048 – real estate contracts; legal documents; service agreements];
generating, by the computing device, instructions to prompt a large language model to generate text data, wherein the instructions are configured to prompt the large language model to extract data from the custom text data for the generation of the text data [para 0051; 0056; 0060-0061; 0064; 0085-0102; 0132 – prompt is constructed]; and
generating, by the computing device using the large language model, the text data, wherein the generated text data is generated based on the custom text data and the instructions [para 0040; 0045; 0052; 0062; 0060-0063; 0085; 0090-0102; -- text summarization].
Regarding claim 2, Padmashali teaches the method as set forth in claim 1, wherein the large language model is trained [para 0047; 0049; 0264] to generate the generated text data using a real estate corpus [para 0047-0048 – real estate contracts; legal documents; service agreements] by: extracting features from the custom text data [para 0051; 0056; 0060-0061; 0064; 0085-0102; 0132 – text/segment/keyword extraction]; classifying the features of the custom text data based on trained features from the real estate corpus [para 0051; 0056; 0060-0061; 0064; 0085-0102; 0132 – classification engines]; and constructing the generated text data based on the instructions and the classifications of the features of the custom text data [para 0040; 0045; 0052; 0062; 0060-0063; 0085; 0090-0102; -- text summarization].
Regarding claim 3, Padmashali teaches the method as set forth in claim 1, wherein the generated text data is a summation of the custom text data or preferences of a client related to the custom text data [para 0040; 0045; 0052; 0062; 0060-0063; 0085; 0090-0102; -- text summarization].
Regarding claim 4, Padmashali teaches the method as set forth in claim 3, wherein the instructions comprise a plurality of predefined topics for the generation of the summation of the custom text data [para 0047-0047 – different document types].
Regarding claims 8-11 and 15-18, claims 8-11 and 15-18 are systems and non-transitory medium claims that are rejected under similar rationale as claims 1-4.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 5-7, 12-14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Padmashali in view of Florance et al (US Patent Application Publication No. 2004/0128215), hereinafter Florance.
Regarding claims 5, 12, and 19, Padmashali teaches the instructions are configured to instruct the large language model to generate the preferences of the client by: extracting the data from the custom text data [para 0051; 0056; 0060-0061; 0064; 0085-0102; 0132 – text/segment/keyword extraction]; and categorizing the extracted data based on a plurality of labels [para 0051; 0056; 0060-0061; 0064; 0085-0102; 0132 – classification engines]. Padmashali fails to teach the plurality of labels comprise property types, locations, ticket size, and a risk profile. In a similar field of endeavor, Florance teaches a system and method for creating a unified commercial real estate data model through collection, distribution and use of information in connection with commercial real estate and for creating a web-based marketplace that facilitates the efficient and secure buying, selling, and leasing of commercial properties, and categories/labels data using property type [para 0061]; locations [para 0061]; ticket size [para 0061 – price range]; and risk profile [para 0033]. One having ordinary skill in the art at the time of the invention would have recognized the advantages of implementing the real estate collection, distribution and use of information, as suggested by Florance, in the system of Padmashali, for the purpose of facilitating the efficient and secure buying, selling, and leasing of commercial properties, as suggested by Florance.
Regarding claims 6 and 13, the combination of Padmashali in view of Florance teaches the method as set forth in claim 5 further comprising: determining, by the computing device, a marketing campaign to recommend to the client based on the preferences generated using the large language model [Florance’s advertising and marketing services -- para 0054-0056; 0060; 0314]; and transmitting, by the computing device to the client device, real estate properties related to the marketing campaign that meet the preferences of the client [Florance’s advertising and marketing services -- para 0054-0056; 0060; 0314].
Regarding claims 7 and 14, the combination of Padmashali in view of Florance teaches the method as set forth in claim 6, wherein each of the real estate properties are determined to meet the preferences of the client by: determining whether a property type, a location, a size, and a predetermined risk profile of one of the real estate properties meets a preferred property type, preferred location, preferred ticket size, and preferred risk profile of the client [Florance para 0033; 0061], wherein the preferred property type, the preferred location, the preferred ticket size, and the preferred risk profile of the client is determined by the computing device based on the categorized extracted data [Padmashali para 0051; 0056; 0060-0061; 0064; 0085-0102; 0132 – text/segment/keyword extraction in combination with Florance para 0042; 0044; 0066; 0080; 0095; 0304 -- system notifies the user by wireless messaging and/or e-mail as soon as a property matches existing search criteria]. ; and in response to determining that the property type, the location, the ticket size, and the predetermined risk profile of the one of the real estate properties meets the preferred property type, preferred location, preferred ticket size, and preferred risk profile of the client, determining that the one of the real estate properties meets the preferences of the client [Florance -- para 0042; 0044; 0066; 0080; 0095; 0304 -- system notifies the user by wireless messaging and/or e-mail as soon as a property matches existing search criteria].
Regarding claim 20, the combination of Padmashali in view of Florance teaches instructions to determine a marketing campaign to recommend to the client based on the preferences generated using the large language model [Florance’s advertising and marketing services -- para 0054-0056; 0060; 0314]; determining whether a property type, a location, a size, and a predetermined risk profile of real estate properties related to the marking campaign meets a preferred property type, preferred location, preferred ticket size, and preferred risk profile of the client [Florance para 0033; 0061], wherein the preferred property type, the preferred location, the preferred ticket size, and the preferred risk profile of the client is determined based on the categorized extracted data [Padmashali’s extraction processing --- para 0051; 0056; 0060-0061; 0064; 0085-0102; 0132 – text/segment/keyword extraction]; and in response to determining that the property type, the location, the ticket size, and the predetermined risk profile of each of the real estate properties meets the preferred property type, preferred location, preferred ticket size, and preferred risk profile of the client, transmit, to the client device, the real estate properties related to the marketing campaign that meet the preferences of the client para 0042; 0044; 0066; 0080; 0095; 0304 -- system notifies the user by wireless messaging and/or e-mail as soon as a property matches existing search criteria].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANGELA A ARMSTRONG whose telephone number is (571)272-7598. The examiner can normally be reached M,T,TH,F 11:30-8:00.
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ANGELA A. ARMSTRONG
Primary Examiner
Art Unit 2659
/ANGELA A ARMSTRONG/Primary Examiner, Art Unit 2659
