DETAILED ACTION
Claims 1-20 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner’s Notes
Examiner has cited particular columns and line numbers, paragraph numbers, or figures in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Statutory Category: Claims 1, 12 and 20 are directed to a method, apparatus and a computer program product. Therefore, the claims are directed to one of the four statutory categories of inventions.
Step 2A – Prong 1: Claims 1, 12 and 20 recites, determining a similarity degree between the code snippet and the at least one difference; determining a usage degree for code containing the at least one difference, based on the similarity degree for the at least one difference. That is, other than a generic computer, nothing in the claim elements precludes the steps from practically being performed mentally. Specifically, determining a similarity degree between the code snippet and the at least one difference; determining a usage degree for code containing the at least one difference, based on the similarity degree for the at least one difference can be performed mentally through observation, evaluation, judgement, opinion by a developer to determine a similarity between code snippets and to determine a usage based on how similar the code snippets are. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the category of abstract idea of mental process. Accordingly, the claim recites an abstract idea under step 2A prong 1.
Step 2A, Prong 2: The additional elements do not integrate the judicial exception into a practical application. The limitations a subject to the usage degree exceeding a predetermined threshold, taking an action, merely recite the field of use/technological environment, see MPEP 2106.05(h). The limitations a computerized apparatus having a processor, the processor being configured to perform the steps of and a computer program product comprising a computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform a method comprising recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). The limitations obtaining a prompt provided to at least one large language model (LLM) for generating programming code; obtaining a code snippet generated by the at least one LLM in response to the prompt; obtaining at least one difference introduced to programmer’s code, add insignificant extra solution activity, such as data gathering and transmission, see MPEP 2106.05(g). Accordingly, the additional elements recited in the claims do not integrate the abstract idea into a practical application.
Step 2B: As discussed with respect to step 2A prong 2, the additional elements a subject to the usage degree exceeding a predetermined threshold, taking an action, merely recite the field of use/technological environment, see MPEP 2106.05(h). The limitations a computerized apparatus having a processor, the processor being configured to perform the steps of and a computer program product comprising a computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform a method comprising recite mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). The limitations obtaining a prompt provided to at least one large language model (LLM) for generating programming code; obtaining a code snippet generated by the at least one LLM in response to the prompt; obtaining at least one difference introduced to programmer’s code amount to well-understood, routine conventional activities as seen in court cases storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 and receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Accordingly, the claim does not amount to significantly more than the judicial exception, thus lack an inventive concept for patent eligibility under 35 USC 101.
Regarding claims 2 and 13 the additional elements wherein the similarity degree is determined subject to the at least one difference being introduced to the programmer’s code since the programmer’s code was checked out of a source control system, further recites an abstract idea. Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claims 3 and 14 the additional elements wherein the similarity degree is determined subject to the prompt being provided to the at least one LLM after a previous commit of the programmer’s code to a source control system, further recites an abstract idea. Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claims 4 and 15 the additional elements wherein said determining is performed upon a commit operation when entering code to a source control system, further recites an abstract idea. Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claims 5 and 16 the additional elements wherein the at least one LLM is a generative artificial intelligence (AI) engine, further recites field of use/technological environment, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claims 5 and 16 the additional elements wherein the at least one LLM is a generative artificial intelligence (AI) engine, further recites field of use/technological environment, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claims 6 the additional elements wherein the code comprises a project, further recites field of use/technological environment, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claims 7 and 17 the additional elements wherein the code comprises a project or a file, further recites field of use/technological environment, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claims 8 and 18 the additional elements wherein the action comprises at least one item selected from the group consisting of: displaying to a user a number of code lines within the code that are based on the code snippet provided by each of the at least one LLM, and displaying to the user code changes attributed to the at least one LLM, add insignificant extra solution activity, such as data gathering and transmission, see MPEP 2106.05(g). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claims 9 and 19 the additional elements wherein the action comprises at least one item selected from the group consisting of: providing to a user an indication that the at least one difference is attributed to the LLM, sending a message to the user, showing code changes, and blocking a build operation or a version creation, add insignificant extra solution activity, such as data gathering and transmission, see MPEP 2106.05(g). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 10 the additional elements further comprising sending a message to a supervisor of the user, to a compliance officer, or to another person in charge, add insignificant extra solution activity, such as data gathering and transmission, see MPEP 2106.05(g). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
Regarding claim 11 the additional elements wherein showing code changes comprises enabling drill down into the code, further recites field of use/technological environment, see MPEP 2106.05(h). Thus, this limitation does not integrate the judicial exception into a practical application under prong 2, or amounts to significantly more under Step 2B.
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) 1-7, 12-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Graves et al. (US-PAT-NO: 12,323,449 B1) hereinafter Graves, in further view of Zanbar et al. (US-PGPUB-NO: 2019/0095315 A1) hereinafter Zanbar.
As per claim 1, Graves teaches a computer-implemented method comprising: obtaining a prompt provided to at least one large language model (LLM) for generating programming code; obtaining a code snippet generated by the at least one LLM in response to the prompt (see Graces [column 122, lines 57-60], “In some embodiments, the one or more constraints may be based on a prompt used to generate the vulnerable code, such as the inclusion of particular keywords or phrases, degrees of similarity between prompts or relative to some reference prompt, and the like. Other constraints may also be used in identifying 2202 the one or more portions of vulnerable code”); obtaining at least one difference introduced to programmer’s code (see Graves [column 123, lines 58-60], “The method of FIG. 22 also includes generating 2206, based on the one or more portions of vulnerable code, one or more portions of suggested replacement code”); determining a similarity degree between the code snippet and the at least one difference (see Graves [column 123, lines 51-57], “In some embodiments, the one or more constraints may be based on a prompt used to generate the vulnerable code, such as the inclusion of particular keywords or phrases, degrees of similarity between prompts or relative to some reference prompt, and the like. Other constraints may also be used in identifying 2202 the one or more portions of vulnerable code”).
Graves does not explicitly teach determining a usage degree for code containing the at least one difference, based on the similarity degree for the at least one difference; and subject to the usage degree exceeding a predetermined threshold, taking an action. However, Zanbar teaches determining a usage degree for code containing the at least one difference, based on the similarity degree for the at least one difference (see Zanbar paragraph [0023], “The way in which the segment identification determines the usage frequencies of the code segments may vary depending on the particular measure of usage frequency used by the segment identification engine 108. The usage frequency of a particular code segment may, for example, specify a number of other code segments of the application code 210 that use, reference, load, or otherwise access the particular code segment.”)7; and subject to the usage degree exceeding a predetermined threshold (see Zanbar paragraph [0026], “Upon determining the usage frequencies of the code segments, the segment identification engine 108 may determine which of the code segments have a usage frequency that satisfies a usage frequency criterion. The usage frequency criterion may specify a threshold value, e.g., a usage frequency criterion satisfied when the usage frequency of a particular code segment exceeds a threshold percentage value (e.g., having a usage frequency percentage greater than 55%)”), taking an action (see Zanbar paragraph [0056], “The usage frequency may specify a ratio of a number of other code segments in the application code that use the particular code segment to a total number of code segments in the application code and the change frequency may specify ratio of a number of commit actions applied to the code segment over a predetermined period of time to a total number of commit applications applied for the application code over the predetermined period of time”).
Graves and Zanbar are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Graves’ teaching of code analysis feedback loop for code created using generative artificial intelligence with Zanbar’s teaching of code coverage thresholds for code segments based on usage frequency and change frequence to incorporate calculating a usage frequence based on change frequency in order to determine what type of action to take the code segment, see Zanbar paragraph [0011], “Code coverage thresholds may have an impact on application development as resources are expended to develop test suites to satisfy the code coverage thresholds applicable to application code. Developing test suites that completely test application code in its entirety may be cumbersome, costly, and an inefficient use of resources, especially as source code of an application changes and evolves during development. Identification of code segments with relatively higher importance or relevance and setting appropriate code coverage thresholds for these code segments may increase testing efficiency and reduce cost. However, the particular code segments that may have a greater impact on the reliability and operation of application code as well as the particular application portions being developed may likewise change during different time periods of an application development process.”
As per claim 2, Graves modified with Zanbar teaches wherein the similarity degree is determined subject to the at least one difference being introduced to the programmer’s code since the programmer’s code was checked out of a source control system (see Graves [column 65, lines 13-23 and lines 39-40], “In some embodiments, the systems described herein may be part of an application performance monitoring (‘APM’) solution. APM software and tools enable the observation of application behavior, observation of its infrastructure dependencies, observation of users and business key performance indicators (‘KPIs’) throughout the application's life cycle, and more. The applications being observed may be developed internally, as packaged applications, as software as a service (‘SaaS’), or embodied in some other ways. In such embodiments, the systems described herein may provide one or more of the following capabilities:” and “Analysis of business KPIs and user journeys (for example, login to check-out)”).
As per claim 3, Graves modified with Zanbar teaches wherein the similarity degree is determined subject to the prompt being provided to the at least one LLM after a previous commit of the programmer’s code to a source control system (see Zanbar paragraph [0028] “A commit action may include any action that saves a change to a code segment, such as code check-ins for a source control program or any other source code version control system”).
As per claim 4, Graves modified with Zanbar teaches wherein said determining is performed upon a commit operation when entering code to a source control system (see Zanbar paragraph [0028] “A commit action may include any action that saves a change to a code segment, such as code check-ins for a source control program or any other source code version control system”).
As per claim 5, Graves modified with Zanbar teaches wherein the at least one LLM is a generative artificial intelligence (AI) engine (see Graves [column 115, lines 48-56], “The method of FIG. 19 includes performing 1902 a code analysis on code generated by a generative artificial intelligence (AI) model. A generative AI model is any model used to perform generative AI functionality. As referred to herein, generative AI uses models such as neural networks, including large language models (LLMs), large multimodal models (LMMs), and the like to generate content, such as text, code, graphics, animations, video, audiovisual representations, audio, speech, etc., in response to prompts”).
As per claim 6, Graves modified with Zanbar teaches wherein the code comprises a project (see Graves [column 56, lines 14-15], “4. join/project/aggregate/subclass of other entities”).
As per claim 7, Graves modified with Zanbar teaches wherein the code comprises a file (see Graves [column 93, lines 8-13], “As such, the data platform may be configured to look at things like command line arguments and know that one or more Java processes with one set of jar files and command line arguments is actually a separate program from one or more Java processes with another set of jar files and command line arguments”).
As per claims 12-16, these are the apparatus having a processor (see Graves [column 7, lines 35-41], “The embodiments described herein can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor”) claims to method claims 1-5, respectively. Therefore, they are rejected for the same reasons as above.
As per claim 17, Graves modified with Zanbar teaches wherein the code comprises a project (see Graves [column 56, lines 14-15], “4. join/project/aggregate/subclass of other entities”) or a file (see Graves [column 93, lines 8-13], “As such, the data platform may be configured to look at things like command line arguments and know that one or more Java processes with one set of jar files and command line arguments is actually a separate program from one or more Java processes with another set of jar files and command line arguments”).
As per claim 20, this is the computer program product comprising a computer readable storage medium (see Graves [column 7, lines 35-41], “The embodiments described herein can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor”) claim to method claim 1. Therefore, it is rejected for the same reasons as above.
Claim(s) 8-11, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Graves (US-PAT-NO: 12,323,449 B1) and Zanbar (US-PGPUB-NO: 2019/0095315 A1), in further view of Smith et al. (US-PGPUB-NO: 2020/0097261 A1) hereinafter Smith.
As per claim 8, Graves modified with Zanbar do not explicitly teach wherein the action comprises at least one item selected from the group consisting of: displaying to a user a number of code lines within the code that are based on the code snippet provided by each of the at least one LLM, and displaying to the user code changes attributed to the at least one LLM. However, Smith teaches wherein the action comprises at least one item selected from the group consisting of: displaying to a user a number of code lines within the code that are based on the code snippet provided by each of the at least one LLM, and displaying to the user code changes attributed to the at least one LLM (see Smith paragraph [0137], “Embodiments may display code snippets in different ways. In one embodiment, code snippets that span multiple lines (“multi-line code snippets”) are shown across multiple lines, for example, in the manner that they would be displayed in the editor. In some embodiments, multi-line code snippets may be displayed on a single line and have characters shown to denote the new lines. This may be easier for viewing and understanding in some cases. In some embodiments, the multi-line code snippet may be come long if it is displayed on a single line. Extra new lines may be inserted at determined locations to make the code snippet easier to read, particularly if the display component is narrower than the width of the code snippet. This may be referred to as a smart wrap feature”).
Graves, Zanbar and Smith are analogous art because they are in the same field of endeavor of software development. Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Graves’ teaching of code analysis feedback loop for code created using generative artificial intelligence and Zanbar’s teaching of code coverage thresholds for code segments based on usage frequency and change frequence with Smith’s teaching of code completion during programming and development to incorporate displaying changes made to code segments using a co-pilot system , see Smith paragraph [0009], “Some embodiments relate to predictive editing. In an exemplary method, an event is detected that is indicative of a need for automatic refactoring. One or more features of the source code may be determined and analyzed. The features may be analyzed to determine that automatic refactoring is needed and to determine the appropriate automatic refactoring action. The automatic refactoring action may be displayed to the user as an option, or may be performed automatically.”
As per claim 9, Graves modified with Zanbar and Smith teaches wherein the action comprises at least one item selected from the group consisting of: providing to a user an indication that the at least one difference is attributed to the LLM, sending a message to the user, showing code changes (see Smith paragraph [0066], “In some embodiments, the source code 310 is buffered in short-term memory and edits or changes are made in the buffered in-memory version of the source code 310 until a save file action is performed to save the updated source code file in permanent memory. In step 404, in parallel with the editor allowing the programming to edit the code, the code completion system 342 waits for an event indicating that a programming co-pilot action, such as code completion or predictive editing, should be performed. An event may be an electronic notification in a computer system indicating that an occurrence or activity has taken place. The triggering event may be of various possible types. For example, the triggering event may be detecting that the user has stopped typing for a specified period of time, detecting that the user has just completed typing a token, detecting that the user has added a new character, deleted a character, or otherwise modified the source code 310, detecting that the user has finished typing a specific character, such as a space, determining that a specified time interval has elapsed since the last code completion suggestion, or other events”), and blocking a build operation or a version creation (see Graves [column 120, lines 47-56], “As is set forth above, in some embodiments, code may be updated (e.g., updated code may be requested) based on the results of multiple previously performed code analyses. For example, a prompt may be generated that indicates any vulnerabilities identified in previous versions of the code or subsets thereof. As another example, a prompt may be generated that requests exclusion of, or includes suggested remedial actions for, any vulnerabilities identified in previous versions of the code or subsets thereof. This may facilitate preventing the reintroduction of vulnerabilities remedied in some previous version of the code”).
As per claim 10, Graves modified with Zanbar and Smith teaches further comprising sending a message to a supervisor of the user, to a compliance officer, or to another person in charge (see Graves [column 92, lines 2-7], “Further assume in this example, however, that examining the source code for the first microservice reveals that the messaging library also includes functions that enable a user of the library to send messages to recipients on an external network using standard internet protocols”).
As per claim 11, Graves modified with Zanbar and Smith teaches wherein showing code changes comprises enabling drill down into the code (see Graves [column 115, lines 3-10], “In this process, a subsequent natural language input 1704 may be intended to drill down on information presented in a previous response 1714. Alternatively, a subsequent natural language input 1704 may be intended to shift directions, introduce different phrasing, or otherwise pivot the direction of the conversation (e.g., if a response 1714 does not adequately address an issue raised by some natural language input 1704)”).
As per claims 18 and 19, these are the apparatus claims to method claims 8 and 9, respectively. Therefore, they are rejected for the same reasons as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Leeman-Munk et al. (US-PAT-NO: 12,277,409 B1) teaches training a code generation model for low-resource languages.
Cowan et al. (US-PGPUB-NO: 2017/0235568 A1) teaches source code revision control with selectable file portion synchronization.
Vargas (US-PGPUG-NO: 2016/0357519 A1) teaches natural language engine for coding and debugging.
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/LENIN PAULINO/Examiner, Art Unit 2197
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