DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
All previous objections and rejections directed to the Applicant’s disclosure and claims not discussed in this Office Action have been withdrawn by the Examiner.
Response to Amendments and Arguments
With respect to the 101 rejections, the applicant argues that the claims recite a “technological solution to a technological problem”, specifically a way to use a large language model to process a wide array of different communication records to generate a coherent and logical output. The inventors of the present application state that identifying diverse records and establishing a framework to generate intelligible analyses of those documents was not available in the technological space at the time. However, the examiner asserts that such technologies, with or without the application of LLMs and/or AI agents, were well-established at the time of invention. The examiner recommends incorporating further technical details into the claims in order to differentiate the applicant’s technology in order to overcome the 101 rejections.
The Applicant’s arguments with respect to the claims have been considered but are moot in view of new grounds for rejection (Agrawal et al.). The amendments now refer to “an AI agent”, which necessitates a new reference.
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 USC 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite steps for an analysis of multi-type communication (i.e., heterogeneous) communication records using large language models.
The limitations of claims 1-20 for an analysis of multi-type communication (i.e., heterogeneous) communication records using large language models, as drafted, describe a method that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. The limitations of claims 1-20 for an analysis of multi-type communication (i.e., heterogeneous) communication records using large language models, as drafted, are a computer program product or apparatus that, under their broadest reasonable interpretation, cover performance of the limitations in the mind but for the recitation of generic computer components. That is, other than “instructions” “computer”, “processor”, and “memory” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the computer hardware language, claim 1 encompasses steps than may be performed manually by the user. Specifically, a person could receive a request to analyze various communication records. The person could sort the various communication records according to types of communication records (i.e., e-mails, transcripts, etc.). The person could first summarize according to a specific type of communication record and then use these summaries to generate a final summary across types of communication records, placing emphasis on certain types/portions of communication records. 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. In particular, claims 1-18 only recite the additional elements “computer readable storage medium”, “instructions” “computer”, “processor”, and “memory” to perform the aforementioned steps. The processor and other hardware are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function for computer-based analysis of multi-type communication (i.e., heterogeneous) communication records using large language models 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 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 hardware elements to perform both the aforementioned steps 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. The claims are not patent eligible.
The same analysis applies to the remaining claims.
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.
Claim(s) 1-6, 9-14, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11861320, hereinafter referred to as Gajek et al., in view of US 20250124024, hereinafter referred to as Kirk., and further in view of US 20250284721, hereinafter referred to as Agrawal et al., support for which is provided by 63563180, hereinafter referred to as Agrawal provisional.
Regarding claim 1 (Currently Amended), Gajek et al. discloses a method comprising:
receiving, by an artificial intelligence ("AI") agent, a request to generate an analysis of communication records, the communication records associated with a plurality of types of communication records ("One or more documents are received at 902. In some embodiments, a document may be uploaded by the client machine. Alternatively, a document may be identified by the client machine, for instance via a link. As still another possibility, a document, may be returned in a search result responsive to a query provided by a client machine. A single summary request may include documents identified and provided in various ways…The user input may be used to generate a summary input message 904, which is sent to the text generation interface system 210,” Gajek et al., col. 20, lines 40-55.);
accessing, by the AI agent, a plurality of communication records associated with the request, each communication record of the plurality of communication records corresponding to one type of the plurality of types of communication records (“In some embodiments, techniques and mechanisms described herein may be used to process an arbitrary number of unique documents (e.g., legal documents) that cannot be accurately parsed and processed via existing optical character recognition and text segmentation solutions,” Gajek et al., col. 5, lines 10-14. Gajek et al. contemplates processing, as an example type of documents, legal documents.);
identifying, by the AI agent using natural language processing functionality and for each type of communication record, a subset of communication records of the respective type of communication records based on the request;
for the respective subset of communication records of a respective type of communication records (“At 906, the text generation interface system 210 determines one or more summarize prompt 908 based on the summary request message 904…The one or more summarize prompts 908 are then sent to the text generation modeling system 270 via one or more summarize prompt messages 912. The text generation modeling system 270 generates one or more raw summaries at 914, which are then sent back to the text generation interface system 210 via one or more summarize response messages at 916,” Gajek et al., col. 20, line 60 – col. 21, line 18. Also, “According to various embodiments, the text generation modeling system 270 may be configured to receive, process, and respond to requests via the communication interface 272, which may be configured to facilitate communications via a network such as the internet. In some embodiments, some or all of the communication with the text generation modeling system 270 may be conducted in accordance with the text generation API 274, which may provide remote access to the text generation model 276. The text generation API 274 may provide functionality such as defining standardized message formatting, enforcing maximum input and/or output size for the text generation model, and/or tracking usage of the text generation model. According to various embodiments, the text generation model 276 may be a large language model. The text generation model 276 may be trained to predict successive words in a sentence. It may be capable of performing functions such as generating correspondence, summarizing text, and/or evaluating search results,” Gajek et al., col. 7, lines 9-24. See also Gajek et al., figs. 2 and 9. Here, an “analysis” is to be interpreted as a (text) summary, as described by para [0015] of the applicant’s specification.);[[,]]
generating, by the AI agent, one or more prompts based on the request and the respective type of communication records, and
generating, by the AI agent using a trained large language model ("LLM"), one or more analyses of the respective communication records comprising submitting the one or more generated prompts and at least a portion of the respective subset of communication records;
for each type of communication record, generating, using, by the AI agent, the trained LLM, a homogeneous analysis of the one or more analyses of the respective subset of communication records corresponding to the respective type of communication records (This is achieved in Gajek et al. via said consolidated summary, as already referred to in the previous step. See also column 23, lines 4-29. The examiner further notes that a subset may be the set itself.);
generating, by the AI agent using the trained LLM, a heterogeneous analysis of the homogeneous analyses of the types of communication records; and
providing, by the AI agent, the heterogeneous analysis in response to the request (“The consolidated summary is provided to the client machine at 936 via a consolidation message. The client machine may then present the consolidated summary as consolidation output at 938,” Gajek et al., col. 23, lines 28-31.).
Claim 1 describes “homogeneous analysis” (i.e., type-related) summaries being summarized into a final summary (called “heterogeneous analysis” in the present application). In other words, claim 1 suggests a processing structure having two phases of summarizing – “homogeneous analysis” for all records/documents within a type (phase 1) and “heterogeneous analysis” among types, i.e., for all (type-related) summaries (phase 2).
Although Gajek et al. does not distinguish between different types of documents and thus does not generate type-related summaries which are themselves summarized, Gajek et al. does exhibit an analogous processing structure in that the raw summaries (“summarize response messages 916”) are first consolidated into a (type-agnostic) “raw consolidation”/set of “consolidation response messages 932” (phase 1) which are then consolidated into a single “consolidation message 934” (phase 2). See Gajek et al., col. 23, lines 28-31, “…if the one or more consolidation response messages include two or more consolidation response messages, each of the different messages may be separately parsed, and the parsed results concatenated to produce a consolidated summary. The consolidated summary is provided to the client machine at 936 via a consolidation message. The client machine may then present the consolidated summary as consolidation output at 938. In the event that further consolidation is required, operations 92-934 may be repeated.”
However, Gajek et al. does not specifically address “a plurality of types of communication records”. Kirk is cited to disclose a plurality of types of communication records (“In some examples, when a user receives the response 265 to the query 215 via the user interface 210 of the client device 205, the user may analyze the information within the response 265. In some examples, since the LLM 220 may include data from multiple data sets 245 and inferences from data obtained from the multiple data sets 245 within the response 265, the user may be unable to determine the origins of the information within the response 265. For example, the data sets 245 may include internal data sets 245 such as personal records from a database, communication (e.g., text messages, emails) threads, or external data sets 245 such as external databases,” Kirk, para [0034].). Kirk benefits Gajek et al. by considering various types of communications records to generate responses to user queries. Therefore, it would be obvious for one skilled in the art to combine the teachings of Gajek et al. with those of Kirk et al. to extend the applicability of the text summarization techniques of Gajek et al.
Neither Gajek et al. nor Kirk et al., though, describe the above features being encompassed by an AI agent.
Agrawal et al. teaches all of the above features of Gajek are encompassed by an AI agent (“Herein are learned automatic triggers for learned summarization, which may be part of proactive ( e.g. autonomous) generative automation to assist technicians with administrative engineering tasks such as troubleshooting. This approach includes mechanisms for personalization and contextualization that facilitate dynamic generation of a linguistic prompt that increases the semantic accuracy and task accuracy of generative inferencing by a large language model (LLM),” Agrawal provisional, p. 2, highlighted section.).
Additionally, Agrawal et al is cited to disclose identifying, by the AI agent using natural language processing functionality and for each type of communication record, a subset of communication records of the respective type of communication records based on the request (“The preparation phase creates a knowledge index of existing structured ( e.g. JSON, XML, and HTML) and unstructured (e.g. prose, word processor) reference documents in a vector store or an indexed database. Whether structured or unstructured, a document may partially or entirely contain natural language such as multiword terms, phrases, sentences, and paragraphs. Each reference document has a fixed-size dense semantic encoding that may, for example, be inferred by the encoder model that uses natural language processing (NLP) to accept an input document as a sequence of lexical tokens. For example, the encoder model may be a large language model (LLM) such as BERT. In an embodiment, the vector store associates each reference document with its fixed-sized encoding that represents the document, referred to herein as a reference encoding,” Agrawal provisional, p. 3, highlighted section.);
generating, by the AI agent, one or more prompts based on the request and the respective type of communication records (“The prompt stage entails sequentially: a) generating (e.g. by the vector store agent) a linguistic prompt based on the search key and the matching documents, b) the generative model accepting the linguistic prompt as input, and c) the generative model inferring (i.e. generating) natural language,” Agrawal provisional, p. 4, highlighted section.), and
generating, by the AI agent using a trained large language model ("LLM"), one or more analyses of the respective communication records comprising submitting the one or more generated prompts and at least a portion of the respective subset of communication records (Agrawal provisional, p. 4, highlighted section.). Agrawal et al. benefits Gajek et al. by providing an autonomous software system that uses LLMs to dynamically generate a linguistic prompt that increases the semantic accuracy and task accuracy of generative inferencing. Therefore, it would be obvious for one skilled in the art to combine the teachings of Gajek et al. with those of Agrawal et al. to improve the text summarization techniques of Gajek et al.
As to claim 9, system claim 9 and method claim 1 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 9 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
As to claim 17, CRM claim 17 and method claim 1 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 17 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
Regarding claim 2 (original), Gajek et al., as modified by Kirk, discloses the method of claim 1, wherein the plurality of types of communication records comprises meeting transcripts, chat logs, emails, meeting or calendar invitations, text messages, or documents (“In some examples, when a user receives the response 265 to the query 215 via the user interface 210 of the client device 205, the user may analyze the information within the response 265. In some examples, since the LLM 220 may include data from multiple data sets 245 and inferences from data obtained from the multiple data sets 245 within the response 265, the user may be unable to determine the origins of the information within the response 265. For example, the data sets 245 may include internal data sets 245 such as personal records from a database, communication (e.g., text messages, emails) threads, or external data sets 245 such as external databases,” Kirk, para [0034].).
As to claim 10, system claim 10 and method claim 2 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 10 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
Regarding claim 3 (original), Gajek et al., as modified by Kirk, discloses the method of claim 1, further comprising, for each communication record of a respective type of communication records, generating an LLM prompt based on the respective type of communication records (“In particular embodiments, a size threshold may be adjusted based on considerations apart from a threshold imposed by an external text generation modeling system. For instance, a text generation interface system may formulate a prompt that includes input text as well as metadata such as one or more instructions for a large language model,” Gajek et al., col. 13, lines 1-6. Here, the prompt includes input text.).
As to claim 11, system claim 11 and method claim 3 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 11 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
As to claim 18, CRM claim 18 and method claim 3 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 18 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
Regarding claim 4 (original), Gajek et al., as modified by Kirk, discloses the method of claim 3, wherein generating the LLM prompt is based on metadata corresponding to the respective communication records corresponding to the respective type of communication records (“In particular embodiments, a size threshold may be adjusted based on considerations apart from a threshold imposed by an external text generation modeling system. For instance, a text generation interface system may formulate a prompt that includes input text as well as metadata such as one or more instructions for a large language model,” Gajek et al., col. 13, lines 1-6.).
As to claim 12, system claim 12 and method claim 4 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 12 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
Regarding claim 5 (original), Gajek et al., as modified by Kirk, discloses the method of claim 1, further comprising, for each type of communication records, responsive to determining that a size of the respective homogeneous analysis satisfies a threshold, using the LLM to re-analyze the respective homogeneous analysis, and wherein generating the homogeneous analysis employs the respective re-analysis of the homogeneous analysis (“Large language models often receive as input a portion of input text and generate in response a portion of output text. In many systems, the large language model imposes a limit on the input text size. Accordingly, in the event that the large language model is asked to summarize a length document, the document may need to be segmented into portions in order to achieve the desired summarization,” Gajek et al., col. 4, lines 1-8. The limit on the input text size serves as a threshold.).
As to claim 13, system claim 13 and method claim 5 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 13 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
Regarding claim 6 (original), Gajek et al., as modified by Kirk, discloses the method of claim 1, wherein generating the heterogeneous analysis comprises providing one or more instructions to the LLM indicating information about the one or more types of communication records (“As one example, a prompt template may include a set of instructions for causing a large language model to generate a correspondence document. The prompt template may be modified to determine a prompt by adding a portion of input text that characterizes the nature of the correspondence document to be generated,” Gajek et al., col. 11, lines 36-41.).
As to claim 14, system claim 14 and method claim 6 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 14 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
Claim(s) 7-8, 15-16, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11861320, hereinafter referred to as Gajek et al., in view of US 20250124024, hereinafter referred to as Kirk, further in view of US 20250284721, hereinafter referred to as Agrawal et al., support for which is provided by 63563180, hereinafter referred to as Agrawal provisional., and further in view of US 12038958, hereinafter referred to as Soubbotin.
Regarding claim 7 (original), Gajek et al., as modified by Kirk, discloses the method of claim 1, but not wherein generating the heterogeneous analysis comprises providing one or more instructions to the LLM indicating a weight for one or more types of communication records. Soubbotin is cited to disclose wherein generating the heterogeneous analysis comprises providing one or more instructions to the LLM indicating a weight for one or more types of communication records (“In contrast, a “multi-document summarization” engine as described herein, analyzes the content of webpages, parses out key fragments and/or concepts from them, ranks/weights them, and generates a (presumably) coherent summary consisting of such fragments collected from multiple webpages,” Soubbotin, col. 19, line 64 – col. 20, line 2.). Soubbotin benefits Gajek et al. by allowing certain document extracts to be given more importance in composing a summary, thereby tailoring a summary to best fit a specific user’s requirements. Therefore, it would be obvious for one skilled in the art to combine the teachings of Gajek et al. with those of Soubbotin to combine the teachings of Gajek et al. with those of Kirk et al. to extend the usefulness of the text summarization techniques of Gajek et al.
As to claim 15, system claim 15 and method claim 7 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 15 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
As to claim 19, CRM claim 19 and method claim 7 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 19 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
Regarding claim 8 (original), Gajek et al., as modified by Kirk, discloses the method of claim 1, but not wherein generating the heterogeneous analysis comprises providing one or more instructions to the LLM indicating a prioritization of the one or more types of communication records. Soubbotin is cited to disclose wherein generating the heterogeneous analysis comprises providing one or more instructions to the LLM indicating a prioritization of the one or more types of communication records (“In contrast, a “multi-document summarization” engine as described herein, analyzes the content of webpages, parses out key fragments and/or concepts from them, ranks/weights them, and generates a (presumably) coherent summary consisting of such fragments collected from multiple webpages,” Soubbotin, col. 19, line 64 – col. 20, line 2. It is noted that a ranking/weighting is a way to prioritize a document.). Soubbotin benefits Gajek et al. by allowing certain document extracts to be given more importance in composing a summary, thereby tailoring a summary to best fit a specific user’s requirements. Therefore, it would be obvious for one skilled in the art to combine the teachings of Gajek et al. with those of Soubbotin to combine the teachings of Gajek et al. with those of Kirk et al. to extend the usefulness of the text summarization techniques of Gajek et al.
As to claim 16, system claim 16 and method claim 8 are related as method and system of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 16 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
As to claim 20, CRM claim 20 and method claim 8 are related as method and CRM of using same, with each claimed element’s function corresponding to the method step. Accordingly claim 20 is similarly rejected under the same rationale as applied above with respect to method claim. Also, Gajek et al., fig. 7 and col. 16, lines 24-40, describes CRM, processor(s), and processor-executable instructions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANNE L THOMAS-HOMESCU whose telephone number is (571)272-0899. The examiner can normally be reached Mon-Fri 8-6.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh M Mehta can be reached on 5712727453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANNE L THOMAS-HOMESCU/Primary Examiner, Art Unit 2656
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