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
Response to Arguments
Applicant's arguments with respect to claims 1-20 have been considered but are moot in view of the new ground(s) of rejection. Applicant’s arguments are directed to the amended subject matter; new prior art is provided below.
Note: The claims are not directed towards patent ineligible subject matter under 35 U.S.C. 101
Step 1: IS THE CLAIM DIRECTED TO A PROCESS, MACHINE, MANUFACTURE OR COMPOSITION OF MATTER?
Yes
Step 2A.1: IS THE CLAIM DIRECTED TO A LAW OF NATURE, A NATURAL PHENOMENON (PRODUCT OF NATURE) OR AN ABSTRACT IDEA?
No
Step 2A.2: DOES THE CLAIM RECITE ADDITIONAL ELEMENTS THAT INTEGRATE THE JUDICIAL EXCEPTION INTO A PRACTICAL APPLICATION?
Yes, if the claims are alternatively construed to be abstract in step 2A1. The claims seek to improve generative output supported by the specification, and reflected by the claims e.g. in spec: 0061 0076. In other words, the claims enable the invention to model processing capacity and efficiency.
Supported by the following:
In Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018), the claimed invention was a method of virus scanning that scans an application program, generates a security profile identifying any potentially suspicious code in the program, and links the security profile to the application program. 879 F.3d at 1303-04, 125 USPQ2d at 1285-86. The Federal Circuit noted that the recited virus screening was an abstract idea, and that merely performing virus screening on a computer does not render the claim eligible. 879 F.3d at 1304, 125 USPQ2d at 1286. The court then continued with its analysis under part one of the Alice/Mayo test by reviewing the patent’s specification, which described the claimed security profile as identifying both hostile and potentially hostile operations. The court noted that the security profile thus enables the invention to protect the user against both previously unknown viruses and “obfuscated code,” as compared to traditional virus scanning, which only recognized the presence of previously-identified viruses. The security profile also enables more flexible virus filtering and greater user customization. 879 F.3d at 1304, 125 USPQ2d at 1286. The court identified these benefits as improving computer functionality, and verified that the claims recite additional elements (e.g., specific steps of using the security profile in a particular way) that reflect this improvement. Accordingly, the court held the claims eligible as not being directed to the recited abstract idea. 879 F.3d at 1304-05, 125 USPQ2d at 1286-87. This analysis is equivalent to the Office’s analysis of determining that the additional elements integrate the judicial exception into a practical application at Step 2A Prong Two, and thus that the claims were not directed to the judicial exception (Step 2A: NO).
Examples of claims that improve technology and are not directed to a judicial exception include: Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339, 118 USPQ2d 1684, 1691-92 (Fed. Cir. 2016) (claims to a self-referential table for a computer database were directed to an improvement in computer capabilities and not directed to an abstract idea); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1315, 120 USPQ2d 1091, 1102-03 (Fed. Cir. 2016) (claims to automatic lip synchronization and facial expression animation were directed to an improvement in computer-related technology and not directed to an abstract idea); Visual Memory LLC v. NVIDIA Corp., 867 F.3d 1253,1259-60, 123 USPQ2d 1712, 1717 (Fed. Cir. 2017) (claims to an enhanced computer memory system were directed to an improvement in computer capabilities and not an abstract idea); Finjan Inc. v. Blue Coat Systems, Inc., 879 F.3d 1299, 125 USPQ2d 1282 (Fed. Cir. 2018) (claims to virus scanning were found to be an improvement in computer technology and not directed to an abstract idea); SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1303 (Fed. Cir. 2019) (claims to detecting suspicious activity by using network monitors and analyzing network packets were found to be an improvement in computer network technology and not directed to an abstract idea). Additional examples are provided in MPEP § 2106.05(a).
Regarding the December 5th 2025 Memo in light of September 26, 2025 Appeals Review Panel Decision in Ex parte Desjardins, Appeal 2024-000567 for Application 16/319,040, in deciding if a recited abstract idea does or does not direct the entire claim to an abstract idea, when a claim is considered as a whole:
Paragraph 21 of the Specification, which the Appellant cites, identifies improvements in training the machine learning model itself. Of course, such an assertion in the Specification alone is insufficient to support a patent eligibility determination, absent a subsequent determination that the claim itself reflects the disclosed improvement. See MPEP § 2106.05(a) (citing Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1316 (Fed. Cir. 2016)). Here, however, we are persuaded that the claims reflect such an improvement. For example, one improvement identified in the 8 Appeal2024-000567 Application 16/319,040 Specification is to "effectively learn new tasks in succession whilst protecting knowledge about previous tasks." Spec. ,r 21. The Specification also recites that the claimed improvement allows artificial intelligence (AI) systems to "us[e] less of their storage capacity" and enables "reduced system complexity." Id. When evaluating the claim as a whole, we discern at least the following limitation of independent claim 1 that reflects the improvement: "adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task." We are persuaded that constitutes an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation. Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing § 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology. Yet, under the panel's reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable "algorithm" and the remaining additional elements as "generic computer components," without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.
Specifically, Ex Parte Desjardins explained the following:
Enfish ranks among the Federal Circuit's leading cases on the eligibility of technological improvements. In particular, Enfish recognized that “[m]uch of the advancement made in computer technology consists of improvements to software that, by their very nature, may not be defined by particular physical features but rather by logical structures and processes.” 822 F.3d at 1339. Moreover, because “[s]oftware can make non-abstract improvements to computer technology, just as hardware improvements can,” the Federal Circuit held that the eligibility determinations should turn on whether “the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.” Id. at 1336. (Desjardins, page 8).
Further in Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention was a method of training a machine learning model on a series of tasks. The Appeals Review Panel (ARP) overall credited benefits including reduced storage, reduced system complexity and streamlining, and preservation of performance attributes associated with earlier tasks during subsequent computational tasks as technological improvements that were disclosed in the patent application specification. Specifically, the ARP upheld the Step 2A Prong One finding that the claims recited an abstract idea (i.e., mathematical concept). In Step 2A Prong Two, the ARP then determined that the specification identified improvements as to how the machine learning model itself operates, including training a machine learning model to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” encountered in continual learning systems. Importantly, the ARP evaluated the claims as a whole in discerning at least the limitation “adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task” reflected the improvement disclosed in the specification. Accordingly, the claims as a whole integrated what would otherwise be a judicial exception instead into a practical application at Step 2A Prong Two, and therefore the claims were
The claim itself does not need to explicitly recite the improvement described in the specification (e.g., “thereby increasing the bandwidth of the channel”). See, e.g., Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), in which the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
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 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over S 12147758 B1 Thomas; Olivia Jean et al. (hereinafter Thomas) in view of US 20250028992 A1 Foley; Myles et al. (hereinafter Foley).
Re claim 1, Thomas teaches
1. A method of improving machine learning model quality by automatically generating training data, comprising: (improve performance analogous to quality col 5 line 60 to col 6 line 5… col 4 line 56 – col 4 line 7 an LLM is trained via learning)
acquiring sample data; (sample of a spreadsheet or table in the input col 4 lines 43-55)
determining a data generation template based on features of the sample data; (features extracted in order to determine intent such as keywords col 4 line 56 to col line 7… and as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
generating question data according to first data in the sample data and the data generation template, and determining answer data according to second data other than the first data in the sample data; and (the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
combining the question data and the answer data into the training data. (the LLM is trained and learns based off of the prompt/response, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
However, while Thomas pairs the answers with other data, it does not necessarily teach the well-known concept of prompt-response pairs for model training, and fails to teach:
and training a machine learning model to generate at least one of text or images using at least the pair of training data. (Foley prompt can be text or image 0066, stored as prompt-response pairs 0022, output can be text or images 0063 with fig. 5)
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 system of Thomas to incorporate the above claim limitations as taught by Foley to allow for use of a known technique to improve similar devices in the same way such as to enhance spreadsheet or general text outputs appended with images, wherein users without advanced spreadsheet skills can generate complex reports with images by simply describing what they want, such that outputting images can be directly into table rows, on their own, as an input image, or as plain text input with text output in or out of spread sheet form, where a user can transform dull numerical reports into visual sheets.
Re claim 9, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
As in fig. 1 of Thomas containing the necessary hardware.
Re claim 15, this claim has been rejected for teaching a broader, or narrower claim based on general inclusion of hardware alone (e.g. processor, memory, instructions), representation of claim 1 omitting/including hardware for instance, otherwise amounting to a virtually identical scope.
As in fig. 1 of Thomas containing the necessary hardware.
Re claims 2, 10, and 16, Thomas teaches
2. The method according to claim 1, wherein the determining a data generation template based on features of the sample data , comprises: (features extracted in order to determine intent such as keywords col 4 line 56 to col line 7… and as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
when the sample data comprises tabular data, determining the data generation template according to arrangement information of data in the tabular data; (user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
when the sample data comprises text data, determining the data generation template by at least one selected from the group consisting of:
performing semantic recognition on the sample data to obtain a semantic recognition result, and determining the data generation template according to the semantic recognition result; and (meaning of the input, user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
performing entity recognition on the sample data through a preset entity recognition model to obtain an entity recognition result, and determining the data generation template according to the entity recognition result. (after the meaning is established, terms such as “sales” or “product” qualify as entities per se col 11 lines 4-16… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
Re claims 3, 11, and 17, Thomas teaches
3. The method according to claim 2, wherein the determining the data generation template according to the semantic recognition result, comprises:
searching for at least one data generation template carrying a first template identifier in a preset template library according to the semantic recognition result, wherein the preset template library pre-stores a plurality of the data generation templates, and the first template identifier is a preset template identifier matching the semantic recognition result; and (using the variety of templates per scenario shown in fig. 8, after the meaning is established, terms such as “sales” or “product” qualify as entities per se col 11 lines 4-16… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
the determining the data generation template according to the entity recognition result, comprises:
searching for at least one data generation template carrying a second template identifier in the preset template library according to the entity recognition result, wherein the second template identifier is a preset template identifier matching the entity recognition result. (using the variety of templates per scenario shown in fig. 8, after the meaning is established, terms such as “sales” or “product” qualify as entities per se col 11 lines 4-16… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
Re claims 4, 12, and 18, Thomas teaches
4. The method according to claim 2, wherein the determining the data generation template according to arrangement information of data in the tabular data, comprises:
determining a question template for acquiring a concept explanation content as the data generation template with respect to any of following data of the tabular data selected from the group consisting of: a table name, first row data, and first column data; and (generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
determining a question template with a condition as the data generation template with respect to non-first row data other than the table name or non-first column data other than the table name in the tabular data. (sorting alters the row and column by user request/prompt to modify or user agreeing to system suggestion thereof… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
Re claims 5, 13, and 19, Thomas teaches
5. The method according to claim 1, wherein the determining a data generation template based on features of the sample data, comprises: (features extracted in order to determine intent such as keywords col 4 line 56 to col line 7… and as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
when the first data in the sample data is a title, determining a first data generation template, wherein the first data generation template is a question template for acquiring an explanation content of the first data; and (the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
the determining answer data according to second data other than the first data in the sample data, comprises:
taking a directory content corresponding to the sample data or an abstract content located after the first data in the sample data as the answer data. (directory as in ordered data, or abstract content such as if the user “what are the sales for each product” without referencing specific data… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
Re claims 6, 14, and 20, Thomas teaches
6. The method according to claim 1, wherein the determining a data generation template based on features of the sample data, comprises: (features extracted in order to determine intent such as keywords col 4 line 56 to col line 7… and as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
when the first data in the sample data is a concept entity, determining a second data generation template, wherein the second data generation template is a question template for acquiring an explanation content of the first data; and (explanation is the response of the system and suggestions… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
the determining answer data according to second data other than the first data in the sample data, comprises:
determining the answer data according to a context content of the first data in the sample data. (in context of any data the user submitted, the system can extrapolate intent when the user asks a vague question with minimal context e.g. “sort sales”… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
Re claim 7, Thomas teaches
7. The method according to claim 1, wherein the determining a data generation template based on features the sample data, comprises: (features extracted in order to determine intent such as keywords col 4 line 56 to col line 7… and as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
when the second data in the sample data is arrayed data, determining a third data generation template, (the table is an array with nth data progressing or reducing as user modifies the table… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
wherein the third data generation template is a question template for acquiring the arrayed data, and the first data is correspondingly data located before the second data in the sample data. (the table is an array in any order and sorted according to user instructions, with nth data progressing or reducing as user modifies the table… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
Re claim 8, Thomas teaches
8. The method according to claim 3, wherein the determining the data generation template according to arrangement information of data in the tabular data, comprises:
determining a question template for acquiring a concept explanation content as the data generation template with respect to any of following data of the tabular data selected from the group consisting of: a table name, first row data, and first column data; and (the table includes column, row, title, etc. and is an array with nth data progressing or reducing as user modifies the table… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
determining a question template with a condition as the data generation template with respect to non-first row data other than the table name or non-first column data other than the table name in the tabular data. (the table can be sorted via user instruction/prompt, and includes column, row, title, etc. and is an array with nth data progressing or reducing as user modifies the table… generalized input as meaning/semantics includes the initial prompt of fig. 7c and entity recognition per se includes the modified semantics/meaning in fig. 7d, e.g. sorting by year or by sale or altering the table as specified in the user prompt, the title also changes, as well as rows, columns, order, etc.… user inputs partial table, system helps arrange it, the prompt is the question and the system response is the answer, utilizing samples of a spreadsheet or table in the input col 4 lines 43-55 as in fig. 6a-6c tabular data with columns and rows with further illustrations in fig. 7a-d, using multiple templates to select in fig. 8 with col 13 line 43 – col 14 line 57 under the premise on an LLM utilizing semantic tags to identify natural language elements including terms, entities, descriptors, titles, subjects, etc.)
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 extension fee 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 date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 12524214 B1 Liguori; Clare E. et al.
Templates for prompt/response AI or LLM systems
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/MICHAEL COLUCCI/Primary Examiner, Art Unit 2655 (571)-270-1847
Examiner FAX: (571)-270-2847
Michael.Colucci@uspto.gov
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