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 .
Priority
The Application claims foreign priority to KR10-2024-0037620 filed 19 March 2024 and KR10-2024-0067186 filed 23 May 2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6 March 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: item 123 (Fig 12). Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 12-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because while claim 12 is directed towards a system, it is noted that the use of the word “system” does not inherently mean that the claim is directed towards a machine or article of manufacture. The system comprises one or more processors and a memory. According to paragraph [0156] of the Specification, the computing system may refer to a virtual machine where the memory and processor may be implemented as virtual hardware. Since the processor and memory are virtual components of a virtual machine, the processor and memory are construed as software per se given the BRI of the terms. Therefore, the claim language fails to provide the necessary hardware required for the claim to fall within the statutory category of a system.
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According to MPEP 2106.03:
As the courts' definitions of machines, manufactures and compositions of matter indicate, a product must have a physical or tangible form in order to fall within one of these statutory categories. Digitech, 758 F.3d at 1348, 111 USPQ2d at 1719. Thus, the Federal Circuit has held that a product claim to an intangible collection of information, even if created by human effort, does not fall within any statutory category. Digitech, 758 F.3d at 1350, 111 USPQ2d at 1720 (claimed "device profile" comprising two sets of data did not meet any of the categories because it was neither a process nor a tangible product). Similarly, software expressed as code or a set of instructions detached from any medium is an idea without physical embodiment. See Microsoft Corp. v. AT&T Corp., 550 U.S. 437, 449, 82 USPQ2d 1400, 1407 (2007); see also Benson, 409 U.S. 67, 175 USPQ2d 675 (An "idea" is not patent eligible). Thus, a product claim to a software program that does not also contain at least one structural limitation (such as a "means plus function" limitation) has no physical or tangible form, and thus does not fall within any statutory category. Another example of an intangible product that does not fall within a statutory category is a paradigm or business model for a marketing company. In re Ferguson, 558 F.3d 1359, 1364, 90 USPQ2d 1035, 1039-40 (Fed. Cir. 2009).
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Even when a product has a physical or tangible form, it may not fall within a statutory category. For instance, a transitory signal, while physical and real, does not possess concrete structure that would qualify as a device or part under the definition of a machine, is not a tangible article or commodity under the definition of a manufacture (even though it is man-made and physical in that it exists in the real world and has tangible causes and effects), and is not composed of matter such that it would qualify as a composition of matter. Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03. As such, a transitory, propagating signal does not fall within any statutory category. Mentor Graphics Corp. v. EVE-USA, Inc., 851 F.3d 1275, 1294, 112 USPQ2d 1120, 1133 (Fed. Cir. 2017); Nuijten, 500 F.3d at 1356-1357, 84 USPQ2d at 1501-03.
Since claims 13-20 are dependent on claim 12 and fail to overcome the deficiencies of claim 12, the claims are rejected on the same grounds as claim 12.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Determining whether claims are statutory under 35 U.S.C. 101 involves a two-step analysis. Step 1 requires a determination of whether the claims are directed to the statutory categories of invention. Step 2 requires a determination of whether the claims are directed to a judicial exception without significantly more. Step 2 is divided into two prongs, with the first prong having a part 1 and part 2. See MPEP 2106.
Claim 1 recites an automatic file name creation method performed by a computing system, the method comprising: acquiring a first file as a file name change target; receiving first setting information about one of semantic information represented by contents of the first file; creating a first prompt instructing to extract first semantic information from the first file based on the first setting information; inputting the first file and the first prompt to a first artificial neural network, and creating a first semantic text corresponding to the first semantic information of the first file based on an output of the first artificial neural network; receiving second setting information specifying a file name creation rule; creating a second prompt instructing to modify the first semantic text according to the file name creation rule based on the second setting information; inputting the first semantic text and the second prompt to a second artificial neural network, and creating a first file name associated with the first semantic text corresponding to the file name creation rule, based on an output of the second artificial neural network; and changing a file name of the first file to the first file name, wherein the first artificial neural network is a large multi-modal model, wherein the second artificial neural network is a large language model.
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Pursuant to MPEP 2106, claims are deemed to be directed to an abstract idea if, under their broadest reasonable interpretation, they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
The limitations of acquiring a first file as a file name change target; receiving first setting information about one of semantic information represented by contents of the first file; creating a first prompt instructing to extract first semantic information from the first file based on the first setting information; creating a first semantic text corresponding to the first semantic information of the first file; receiving second setting information specifying a file name creation rule; creating a second prompt instructing to modify the first semantic text according to the file name creation rule based on the second setting information; creating a first file name associated with the first semantic text corresponding to the file name creation rule; and changing a file name of the first file to the first file name, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgement, opinion) except for the recitation of generic computer components. For example, these limitations depict a person deciding a file to change a name of, identifying information of the photo, writing a prompt and using the prompt and the information to create a description of the photo. Next the person can come up with the format for the filename and write a second prompt. The second prompt and description can then be used to create a filename. If limitations, under their broadest reasonable interpretation, covers the performance of the limitation in the mind except for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1).
This judicial exception is not integrated into a practical application. The claim recites the additional elements of a computing system, a first artificial neural network, wherein the first artificial neural network is a large multi-modal model and a second artificial neural network, wherein the second artificial neural network is a large language model. The elements are recited at a high level of generality (i.e., a generic computer performing the generic computer functions of executing a method and a generic LMM and LLM to perform the generic functions of receiving input and generating output) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The claim also recites the additional elements of acquiring, receiving, inputting and outputting. These elements are adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since the elements are gathering data and outputting data. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Pursuant to Step 2B, claims are analyzed to determine whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the additional elements of a computing system, a first artificial neural network, wherein the first artificial neural network is a large multi-modal model and a second artificial neural network, wherein the second artificial neural network is a large language model. The elements are recited at a high level of generality (i.e., a generic computer performing the generic computer functions of executing a method and a generic LMM and LLM to perform the generic functions of receiving input and generating output) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The claim also recites the additional elements of acquiring, receiving, inputting and outputting. These elements are adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since the elements are gathering data and outputting data. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). The limitation is directed to IESA of gathering and outputting data, e.g., using the Internet to gather data, performing repetitive calculations, electronic recordkeeping, storing and retrieving information in memory, electronically scanning or extracting data from a physical document, a web browser’s back and forward button functionality, recording a customer’s order, shuffling and dealing a standard deck of cards, restricting public access to media by requiring a consumer to view an advertisement, presenting offers and gathering statistics, determining an estimated outcome and setting a price, arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, which is well understood, routine, and conventional. See MPEP 2106.05(d), subsection II and the Berkheimer Memo. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not patent eligible.
Claim 12 recites an automatic file name creation system comprising: one or more processors; and a memory storing therein a computer program executed by the one or more processors, wherein the computer program includes instructions for: acquiring a first file as a file name change target; receiving first setting information about one of semantic information represented by contents of the first file; creating a first prompt instructing to extract first semantic information from the first file based on the first setting information; inputting the first file and the first prompt to a first artificial neural network, and creating a first semantic text corresponding to the first semantic information of the first file based on an output of the first artificial neural network; receiving second setting information specifying a file name creation rule; creating a second prompt instructing to modify the first semantic text according to the file name creation rule based on the second setting information; inputting the first semantic text and the second prompt to a second artificial neural network, and creating a first file name associated with the first semantic text corresponding to the file name creation rule, based on an output of the second artificial neural network; and changing a file name of the first file to the first file name, wherein the first artificial neural network is a large multi-modal model, wherein the second artificial neural network is a large language model.
Pursuant to Step 2A, part 1, claims are analyzed to determine whether they are directed to an abstract idea. Pursuant to MPEP 2106, claims are deemed to be directed to an abstract idea if, under their broadest reasonable interpretation, they fall within one of the enumerated categories of (a) mathematical concepts, (b) certain methods of organizing human activity, and (c) mental processes. Under the broadest reasonable interpretation, the terms of the claim are presumed to have their plain meaning consistent with the specification as it would be interpreted by one of ordinary skill in the art. See MPEP 2111.
The limitations of acquiring a first file as a file name change target; receiving first setting information about one of semantic information represented by contents of the first file; creating a first prompt instructing to extract first semantic information from the first file based on the first setting information; creating a first semantic text corresponding to the first semantic information of the first file; receiving second setting information specifying a file name creation rule; creating a second prompt instructing to modify the first semantic text according to the file name creation rule based on the second setting information; creating a first file name associated with the first semantic text corresponding to the file name creation rule; and changing a file name of the first file to the first file name, as drafted, are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind (including an observation, evaluation, judgement, opinion) except for the recitation of generic computer components. For example, these limitations depict a person deciding a file to change a name of, identifying information of the photo, writing a prompt and using the prompt and the information to create a description of the photo. Next the person can come up with the format for the filename and write a second prompt. The second prompt and description can then be used to create a filename. If limitations, under their broadest reasonable interpretation, covers the performance of the limitation in the mind except for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Pursuant to Step 2A, part 2, claims are analyzed to determine whether the claim as a whole integrates the recited judicial exception into a practical application of the exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). One way to determine integration into a practical application is when the claimed invention improves the functioning of a computer or improves another technology or technical field. To evaluate an improvement to a computer or technical field, the specification must set forth an improvement in technology and the claim itself must reflect the disclosed improvement. See MPEP 2106.04(d)(1).
This judicial exception is not integrated into a practical application. The claim recites the additional elements of processors, a memory, a first artificial neural network, wherein the first artificial neural network is a large multi-modal model and a second artificial neural network, wherein the second artificial neural network is a large language model. The elements are recited at a high level of generality (i.e., a generic computer performing the generic computer functions of executing a method and a generic LMM and LLM to perform the generic functions of receiving input and generating output) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The claim also recites the additional elements of acquiring, receiving, inputting and outputting. These elements are adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since the elements are gathering data and outputting data. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Pursuant to Step 2B, claims are analyzed to determine whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim recites the additional elements of processors, memory, a computing system, a first artificial neural network, wherein the first artificial neural network is a large multi-modal model and a second artificial neural network, wherein the second artificial neural network is a large language model. The elements are recited at a high level of generality (i.e., a generic computer performing the generic computer functions of executing a method and a generic LMM and LLM to perform the generic functions of receiving input and generating output) such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). The claim also recites the additional elements of acquiring, receiving, inputting and outputting. These elements are adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)) since the elements are gathering data and outputting data. The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent. MPEP 2106.05(g). At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). The limitation is directed to IESA of gathering and outputting data, e.g., using the Internet to gather data, performing repetitive calculations, electronic recordkeeping, storing and retrieving information in memory, electronically scanning or extracting data from a physical document, a web browser’s back and forward button functionality, recording a customer’s order, shuffling and dealing a standard deck of cards, restricting public access to media by requiring a consumer to view an advertisement, presenting offers and gathering statistics, determining an estimated outcome and setting a price, arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, which is well understood, routine, and conventional. See MPEP 2106.05(d), subsection II and the Berkheimer Memo. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not patent eligible.
Claims 6-10 and 16-19 are directed to the abstract idea of “Mental Processes.” The additional limitations are also directed to a “Mental Process.” Each claim fails to provide any additional elements. This judicial exception is not integrated into a practical application because there are no additional elements to integrate the abstract idea into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because there are no additional elements. The claims are not patent eligible.
Claim 11 is directed to the abstract idea of “Mental Processes.” The additional limitations of each of the claims is directed to adding insignificant extra-solution activity to the judicial exception (see MPEP 2106.05(g)). 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. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). The limitation is directed to IESA, e.g., using the Internet to gather data, performing repetitive calculations, electronic recordkeeping, storing and retrieving information in memory, electronically scanning or extracting data from a physical document, a web browser’s back and forward button functionality, recording a customer’s order, shuffling and dealing a standard deck of cards, restricting public access to media by requiring a consumer to view an advertisement, presenting offers and gathering statistics, determining an estimated outcome and setting a price, arranging a hierarchy of groups, sorting information, eliminating less restrictive pricing information and determining the price, which is well understood, routine, and conventional. See MPEP 2106.05(d), subsection II and the Berkheimer Memo. Even when considered in combination, these additional elements represent insignificant extra-solution activity which does not provide an inventive concept.
Claims 2, 3, 13 and 20 are directed to the abstract idea of “Mental Processes.” The additional elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (see MPEP 2106.05(f)). These additional elements do not integrate the judicial exception into a practical application. The additional elements are not sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 4, 5, 14 and 15 are directed to the abstract idea of “Mental Processes.” The additional elements are generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). These additional elements do not integrate the judicial exception into a practical application. The additional elements are not sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
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.
Claim(s) 1, 3, 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPub 2023/0062307 to Ramsl (hereafter Ramsl) in view of the article “Using Generative AI to improve Image Filenames” to Raymond Camden (hereafter Camden).
Referring to claim 1, Ramsl discloses an automatic file name creation method performed by a computing system, the method comprising:
acquiring a first file [FILE.JPEG] as a file name change target (see [0015]; [0064] – A user may identify multiple files to be named. For example, a folder or file system may be selected by a user or administrator for processing.);
inputting the first file to a first artificial neural network, and creating a first semantic text corresponding to the first semantic information of the first file based on an output of the first artificial neural network (see [0028]; [0031]; [0034]; [0066]; Fig 7 – The evaluation module may determine, based on the type of first file, how to obtain information about the first file to be used by the naming module to name a copy of the first file for smart document management. In the example of the method 700, based on the first file being an image file, the evaluation module uses a trained machine learning model to identify an object depicted in the first file (operation 720). For example, the image file may be an image of an orange and the trained machine learning model may generate an output of “fruit,” or “orange.” A neural network, sometimes referred to as an artificial neural network …. Such systems progressively improve performance, which is referred to as learning, to perform tasks, typically without task-specific programming. For example, in image recognition, a neural network may be taught to identify images that contain an object by analyzing example images that have been tagged with a name for the object and, having learnt the object and name, may use the analytic results to identify the object in untagged images.);
receiving second setting information specifying a file name creation rule (see [0029]; [0056]-[0058]);
inputting the first semantic text and creating a first file name associated with the first semantic text corresponding to the file name creation rule (see [0029]; [0060]-[0062] – The naming module 240 copies files from the file system server to the file system server and selects the names for the copied files based on data received from the evaluation module. For example, files may be named with a date prefix in YYYY-MM-DD format, followed by a topic word or phrase. The date may be the creation date of the file, as determined from metadata for the file in the file system server. The topic word or phrase may be determined from the contents of the file.), based on an output of the second artificial neural network; and
changing a file name of the first file to the first file name (see [0068] – The naming module 240, in operation 740 names the copy of the first file based on the identified object. For example, the input file “FILE.JPEG” may be renamed to “ORANGE.JPEG” when copied.),
While Ramsl discloses renaming a file based on content of the file using an artificial neural network, Ramsl fails to explicitly disclose the further limitations of receiving first setting information about one of semantic information represented by contents of the first file; creating a first prompt instructing to extract first semantic information from the first file based on the first setting information; creating a second prompt instructing to modify the first semantic text according to the file name creation rule based on the second setting information; a second artificial neural network,
wherein the first artificial neural network is a large multi-modal model, and wherein the second artificial neural network is a large language model. Camden teaches using generative AI to improve image filenames, including the further limitations of
receiving first setting information about one of semantic information represented by contents of the first file (see page 2 – Write a one sentence short summary of this image.);
creating a first prompt instructing to extract first semantic information from the first file based on the first setting information (see page 2 – Write a one sentence short summary of this image. The sentence should be no more than five words.);
inputting the first file and the first prompt [“Write a one sentence short summary of this image. The sentence should be no more than five words.”] to a first artificial neural network [Gemini], and creating a first semantic text corresponding to the first semantic information of the first file based on an output of the first artificial neural network (see page 3 – async function getImageSummary(path));
creating a second prompt instructing to modify the first semantic text according to the file name creation rule based on the second setting information (see page 4, code lines 68-77 – The file name creation rule is let newname = OUTPUT + slugify(result) + ‘.jpg.);
inputting the first semantic text and the second prompt to a second artificial neural network, and creating a first file name associated with the first semantic text corresponding to the file name creation rule, based on an output of the second artificial neural network (see page 4, code lines 68-77); and
changing a file name of the first file to the first file name (see pages 5-7),
wherein the first artificial neural network [Gemini] is a large multi-modal model (see page 2 - Gemini is an example of a large multi-modal model as is confirmed by paragraph [0087] of Applicant’s published specification.),
wherein the second artificial neural network [Gemini] is a large language model (see page 2 - Gemini is an example of a large language model as is confirmed by paragraph [0095] of Applicant’s published specification.).
Ramsl and Camden are analogous art since they both rename files based on content in the files. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to utilize the prompts and code of Camden to execute the process of Ramsl for renaming files. One would have been motivated to do so to consistently name files in a meaningful way based on the content of the files (Ramsl: see [0017]; Camden: see page 1).
Referring to claim 3, the combination of Ramsl and Camden (hereafter Ramsl/Camden) teaches the automatic file name creation method of claim 1, wherein when the first file has an image format [based on the first file being an image file], the first semantic information is information related to a first object included in an image of the first file [fruit or orange], wherein the creating of the first semantic text corresponding to the first semantic information includes:
inputting the first file and the first prompt to the first artificial neural network [evaluation module] (Ramsl: see [0028]; [0031]; [0034]; [0066]; Fig 7 – A neural network, sometimes referred to as an artificial neural network …. Such systems progressively improve performance, which is referred to as learning, to perform tasks, typically without task-specific programming. For example, in image recognition, a neural network may be taught to identify images that contain an object by analyzing example images that have been tagged with a name for the object and, having learnt the object and name, may use the analytic results to identify the object in untagged images.); and
creating the first semantic text associated with the first object, based on the output of the first artificial neural network (Ramsl: see [0028]; [0031]; [0034]; [0066]; Fig 7 – The evaluation module may determine, based on the type of first file, how to obtain information about the first file to be used by the naming module to name a copy of the first file for smart document management. In the example of the method 700, based on the first file being an image file, the evaluation module uses a trained machine learning model to identify an object depicted in the first file (operation 720). For example, the image file may be an image of an orange and the trained machine learning model may generate an output of “fruit,” or “orange.”).
Referring to claim 12, Ramsl discloses an automatic file name creation system comprising:
acquiring a first file [FILE.JPEG] as a file name change target (see [0015]; [0064] – A user may identify multiple files to be named. For example, a folder or file system may be selected by a user or administrator for processing.);
inputting the first file to a first artificial neural network, and creating a first semantic text corresponding to the first semantic information of the first file based on an output of the first artificial neural network (see [0028]; [0031]; [0034]; [0066]; Fig 7 – The evaluation module may determine, based on the type of first file, how to obtain information about the first file to be used by the naming module to name a copy of the first file for smart document management. In the example of the method 700, based on the first file being an image file, the evaluation module uses a trained machine learning model to identify an object depicted in the first file (operation 720). For example, the image file may be an image of an orange and the trained machine learning model may generate an output of “fruit,” or “orange.” A neural network, sometimes referred to as an artificial neural network …. Such systems progressively improve performance, which is referred to as learning, to perform tasks, typically without task-specific programming. For example, in image recognition, a neural network may be taught to identify images that contain an object by analyzing example images that have been tagged with a name for the object and, having learnt the object and name, may use the analytic results to identify the object in untagged images.);
receiving second setting information specifying a file name creation rule (see [0029]; [0056]-[0058]);
inputting the first semantic text and creating a first file name associated with the first semantic text corresponding to the file name creation rule (see [0029]; [0060]-[0062] – The naming module 240 copies files from the file system server to the file system server and selects the names for the copied files based on data received from the evaluation module. For example, files may be named with a date prefix in YYYY-MM-DD format, followed by a topic word or phrase. The date may be the creation date of the file, as determined from metadata for the file in the file system server. The topic word or phrase may be determined from the contents of the file.), based on an output of the second artificial neural network; and
changing a file name of the first file to the first file name (see [0068] – The naming module 240, in operation 740 names the copy of the first file based on the identified object. For example, the input file “FILE.JPEG” may be renamed to “ORANGE.JPEG” when copied.),
While Ramsl discloses renaming a file based on content of the file using an artificial neural network, Ramsl fails to explicitly disclose the further limitations of receiving first setting information about one of semantic information represented by contents of the first file; creating a first prompt instructing to extract first semantic information from the first file based on the first setting information; creating a second prompt instructing to modify the first semantic text according to the file name creation rule based on the second setting information; a second artificial neural network,
wherein the first artificial neural network is a large multi-modal model, and wherein the second artificial neural network is a large language model. Camden teaches using generative AI to improve image filenames, including the further limitations of
one or more processors (see [0025]); and
a memory storing therein a computer program executed by the one or more processors (see [0025]), wherein the computer program includes instructions for:
receiving first setting information about one of semantic information represented by contents of the first file (see page 2 – Write a one sentence short summary of this image.);
creating a first prompt instructing to extract first semantic information from the first file based on the first setting information (see page 2 – Write a one sentence short summary of this image. The sentence should be no more than five words.);
inputting the first file and the first prompt [“Write a one sentence short summary of this image. The sentence should be no more than five words.”] to a first artificial neural network [Gemini], and creating a first semantic text corresponding to the first semantic information of the first file based on an output of the first artificial neural network (see page 3 – async function getImageSummary(path));
creating a second prompt instructing to modify the first semantic text according to the file name creation rule based on the second setting information (see page 4, code lines 68-77 – The file name creation rule is let newname = OUTPUT + slugify(result) + ‘.jpg.);
inputting the first semantic text and the second prompt to a second artificial neural network, and creating a first file name associated with the first semantic text corresponding to the file name creation rule, based on an output of the second artificial neural network (see page 4, code lines 68-77); and
changing a file name of the first file to the first file name (see pages 5-7),
wherein the first artificial neural network [Gemini] is a large multi-modal model (see page 2 - Gemini is an example of a large multi-modal model as is confirmed by paragraph [0087] of Applicant’s published specification.),
wherein the second artificial neural network [Gemini] is a large language model (see page 2 - Gemini is an example of a large language model as is confirmed by paragraph [0095] of Applicant’s published specification.).
Ramsl and Camden are analogous art since they both rename files based on content in the files. It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to utilize the prompts and code of Camden to execute the process of Ramsl for renaming files. One would have been motivated to do so to consistently name files in a meaningful way based on the content of the files (Ramsl: see [0017]; Camden: see page 1).
Referring to claim 13, Ramsl/Camden teaches the automatic file name creation system of claim 12, wherein when the first file has an image format [based on the first file being an image file], the first semantic information is information related to a first object included in an image of the first file [fruit or orange], wherein the creating of the first semantic text corresponding to the first semantic information includes:
inputting the first file and the first prompt to the first artificial neural network [evaluation module] (Ramsl: see [0028]; [0031]; [0034]; [0066]; Fig 7 – A neural network, sometimes referred to as an artificial neural network …. Such systems progressively improve performance, which is referred to as learning, to perform tasks, typically without task-specific programming. For example, in image recognition, a neural network may be taught to identify images that contain an object by analyzing example images that have been tagged with a name for the object and, having learnt the object and name, may use the analytic results to identify the object in untagged images.); and
creating the first semantic text associated with the first object, based on the output of the first artificial neural network (Ramsl: see [0028]; [0031]; [0034]; [0066]; Fig 7 – The evaluation module may determine, based on the type of first file, how to obtain information about the first file to be used by the naming module to name a copy of the first file for smart document management. In the example of the method 700, based on the first file being an image file, the evaluation module uses a trained machine learning model to identify an object depicted in the first file (operation 720). For example, the image file may be an image of an orange and the trained machine learning model may generate an output of “fruit,” or “orange.”).
Claim(s) 2 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPub 2023/0062307 to Ramsl (hereafter Ramsl) in view of the article “Using Generative AI to improve Image Filenames” to Raymond Camden (hereafter Camden) as applied to claims 1 and 12 above, and further in view of US PGPub 2024/0096122 to Mohandoss et al (hereafter Mohandoss).
Referring to claims 2 and 20, Ramsl/Camden fails to explicitly teach the further limitations of identifying that a user uploads the first file in a first chatting session via a message application; and inputting a context of the first chatting session and the first file to the first artificial neural network, and creating the first semantic text corresponding to the context of the first chatting session, based on the output of the first artificial neural network. Mohandoss teaches
identifying that a user uploads the first file in a first chatting session via a message application (see [0041] – user can attach images in a chat); and
inputting a context of the first chatting session and the first file to the first artificial neural network, and creating the first semantic text corresponding to the context of the first chatting session, based on the output of the first artificial neural network (see [0046]; [0048]; and [0050]).
It would have been obvious to one of ordinary skill in the art at the prior to the effective filing date of the claimed invention to create the semantic text of Ramsl/Camden using a chat session as taught by Mohandoss. One would have been motivated to do so to consistently name files in a meaningful way based on the content of the files (Ramsl: see [0017]; Mohandoss: see [0004]).
Claim(s) 4-9 and 14-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US PGPub 2023/0062307 to Ramsl (hereafter Ramsl) in view of the article “Using Generative AI to improve Image Filenames” to Raymond Camden (hereafter Camden) as applied to claims 1 and 12 above, and further in view of US Patent No 8,078,603 to Chandratillake et al (hereafter Chandra).
Referring to claims 4 and 14, Ramsl/Camden fails to explicitly teach the further limitation wherein when the first file has a video format, the first semantic information is information related to a motion of an object included in a video of the first file. Chandra teaches filtering files including the further limitation wherein when the first file has a video format, the first semantic information is information related to a motion of an object included in a video of the first file (see column 9, lines 4-36).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the claimed invention to select the files of Ramsl/Camden to further process using the process for filtering files taught by Chandra. One would have been motivated to do so to quickly and sufficiently sort through a large number of files for a particular attribute (Chandra: see column 9, lines 4-36).
Referring to claims 5 and 15, Ramsl/Camden fails to explicitly teach the further limitation wherein when the first file has an audio format, the first semantic information is information related to a script of an utterance included in an audio of the first file. Chandra teaches filtering files including the further limitation wherein when the first file has an audio format, the first semantic information is information related to a script of an utterance included in an audio of the first file (see column 9, lines 4-36).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the claimed invention to select the files of Ramsl/Camden to further process using the process for filtering files taught by Chandra. One would have been motivated to do so to quickly and sufficiently sort through a large number of files for a particular attribute (Chandra: see column 9, lines 4-36).
Referring to claims 6 and 16, while Ramsl/Camden teaches selecting a file name charge target (see [0015] and [0064]), Ramsl/Camden fails to explicitly teach the further limitation wherein the automatic file name creation method of claim 1, wherein the acquiring of the first file as the file name change target includes: determining, as the file name change target, the first file corresponding to third setting information among an acquired plurality of files, based on the third setting information input by a user. Chandra teaches filtering files including the further limitation of determining, as the file name change target, the first file corresponding to third setting information among an acquired plurality of files, based on the third setting information input by a user (see column 9, lines 4-36).
It would have been obvious to one of ordinary skill in the art prior to the effective filling date of the claimed invention to select the files of Ramsl/Camden to further process using the process for filtering files taught by Chandra. One would have been motivated to do so to quickly and sufficiently sort through a large number of files for a particular attribute (Chandra: see column 9, lines 4-36).
Referring to claim 7, the combination of Ramsl/Camden and Chandra (hereafter Ramsl/Camden/Chandra) teaches the automatic file name creation method of claim 6, wherein the determining of the first file corresponding to the third setting information among the acquired plurality of files as the file name change target, based on the third setting information input by the user includes: determining, as the file name change target, the first file including a first object specified by the third setting information among an acquired plurality of image format files (Chandra: see column 9, lines 4-36).
Referring to claim 8, Ramsl/Camden/Chandra teaches the automatic file name creation method of claim 6, wherein the determining of the first file corresponding to the third setting information among the acquired plurality of files as the file name change target, based on the third setting information input by the user includes: determining, as the file name change target, the first file including an object performing a first motion specified by the third setting information among an acquired plurality of video format files (Chandra: see column 9, lines 4-36).
Referring to claim 9, Ramsl/Camden/Chandra teaches the automatic file name creation method of claim 6, wherein the determining of the first file corresponding to the third setting information among the acquired plurality of files as the file name change target, based on the third setting information input by the user includes: determining, as the file name change target, the first file including utterance of a first speaker specified by the third setting information among an acquired plurality of audio format files (Chandra: see column 9, lines 4-36).
Referring to claim 17, Ramsl/Camden/Chandra teaches the automatic file name creation system of claim 16, wherein the determining of the first file corresponding to the third setting information among the acquired plurality of files as the file name change target, based on the third setting information input by the user includes: determining, as the file name change target, the first file including a first object specified by the third setting information among an acquired plurality of image format files (Chandra: see column 9, lines 4-36).
Referring to claim 18, Ramsl/Camden/Chandra teaches the automatic file name creation system of claim 16, wherein the determining of the first file corresponding to the third setting information among the acquired plurality of files as the file name change target, based on the third setting information input by the user includes: determining, as the file name change target, the first file including an object performing a first motion specified by the third setting information among an acquired plurality of video format files (Chandra: see column 9, lines 4-36).
Referring to claim 19, Ramsl/Camden/Chandra teaches the automatic file name creation system of claim 16, wherein the determining of the first file corresponding to the third setting information among the acquired plurality of files as the file name change target, based on the third setting information input by the user includes: determining, as the file name change target, the first file including utterance of a first speaker specified by the third setting information among an acquired plurality of audio format files (Chandra: see column 9, lines 4-36).
Allowable Subject Matter
Claims 10 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PGPub 2024/0428068 to Murakhovs’ka teaches feeding from a first model to a second model with each model having a prompt.
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/KIMBERLY L WILSON/Primary Examiner, Art Unit 2165