DETAILED ACTION
This non-final office action is responsive to application 17/871,944 as submitted 24 July 2022.
Claim status is currently pending and under examination for claims 1-11 of which independent claims are 1 and 7-11. The independent claims are grouped as group I: claims 1 & 7-8, and group II: 9-11 both groups having at least some overlap are thus being examined in entirety.
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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The application has an effective filing date of 09/15/2021.
Information Disclosure Statement
As required by M.P.E.P. 609(c), the applicant’s submissions of the Information Disclosure Statements dated 07/24/22 – 12/19/23 are acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P. 609 C(2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Specification
The disclosure is objected to because the title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed, see MPEP 606.01.
The disclosure is objected to because of the following informalities: paragraphs [0028, 88] recite “The model is, for example, the program.” This description should be corrected so as to be consistent with claims 9-11 and 3-4 where the model enables generation of a program in the limitations of training and/or generating. Equating or redefining the model as the program in an embodiment serves to hinder readability of a claim which recites both model and program as two separate elements of the claim. Appropriate correction is required.
Drawings
The drawings are objected to for the minor informality at Fig. 13:S1302 incomplete description and should follow specification [0167]. Corrected drawing sheets in compliance with 37 CFR 1.121(d) 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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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 Objections
Claim 8 is objected to because of the following informality: preamble recites “An information processing comprising: a memory; and a processor” which should be corrected as “An information processing device comprising: a memory; and a processor” which reflects preamble similar to claim 11. Appropriate correction is required.
Claim 4 is objected to because of the following informality: limitation of training recites “the predetermined number of selected programs” should be “
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention. Particularly, claims 1 and 5-11 recite the phrase “relatively great acquired performance” where the term “relatively great” is a relative term which renders the claim indefinite. The term “relatively great” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. Therefore, claims 1 and 5-11 are indefinite. For purposes of examination, interpretation broadly considers comparative measures and may comprise threshold or ranking criteria. Claims 2-4 depend from claim 1 without curing the deficiency. Accordingly, claims 1-11 are rejected as indefinite under 35 U.S.C. 112(b).
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. In determining whether the claims are subject matter eligible, the examiner applies guidance set forth under MPEP 2106.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter? Yes—all claims fall within one of the four statutory categories: claims 1-6 and 9 are a computer readable recording medium/article of manufacture, claims 7 and 10 are a method/process, claims 8 and 11 are (or could be amended to be) a device/machine. Thus, the analysis should proceed per MPEP 2106.03.
Step 2A, prong one: Does the claim recite an abstract idea, law of nature or natural phenomenon? Yes—the claims, under the broadest reasonable interpretation, recites an abstract idea. In this case, claims fall within the enumerated grouping of abstract idea being “Mental Processes” but for the recitation of generic computer components. In particular, claims recite:
Claims 1 and 7-8
“acquiring a performance value that represents performance of each of the programs” (mental process, empirical performance evaluation e.g. trial-and-error)
“classifying the program group into a plurality of clusters on the basis of the acquired features” (mental process, judgment of groupings e.g. discriminating by similarity)
“selecting, from each cluster of the plurality of clusters, one or more of programs that have relatively great acquired performance values” (mental process, judgment or opinion such as comparison for choice based on performance)
Claims 9-11
“acquiring a performance value that represents performance of each of programs of a program group that includes the acquired first program and the generated second program” (mental process, empirical performance evaluation e.g. trial-and-error)
“selecting, from the program group, one or more of programs that have relatively great acquired performance values” (mental process, judgment or opinion such as comparison for choice based on performance)
Focus of the claims concern program selection based on acquired performance, and in the case of claims 1 and 7-8, classifying the programs. When read in light of the specification, programs are [0030] “programs created by an analyst” or [0062] “developer who creates the program” and [0047] “program 111 that reflects intention of the user.” Such an analyst, developer or user is a human whom may perform the functions of selecting programs based on prior performance and classify the programs. No strict definitions are apparent to preclude mental processes. Therefore, claims are drawn to mental processes as the abstract idea, MPEP 2106.04(a)(2)(III).
Step 2A, prong two: Does the claim recite additional elements that integrate the judicial exception into a practical application? No—a practical application is not integrated by the judicial exception because the additional elements are as follows:
Claims 1 and 7-8
“acquiring features that represent contents of each of programs of a program group related to a certain field” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. mere data gathering as a pre-solution step
“outputting the one or more of programs” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. necessary data outputting, post-solution
Claims 9-11
“acquiring a first program related to a certain field” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. mere data gathering or selecting a particular data source or type of data to be manipulated
“training, on the basis of the acquired first program, a first model that enables generation of a new program related to the field according to data features related to the field” MPEP 2106.05(f)(g) adding the words ‘apply it’ (or an equivalent) with the judicial exception and/or adding insignificant extra-solution activity to the judicial exception
“generating a second program related to the field by using the trained first model on the basis of the data features related to the field” MPEP 2106.05(f)(g) adding the words ‘apply it’ (or an equivalent) with the judicial exception and/or adding insignificant extra-solution activity to the judicial exception
“outputting the one or more of programs” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. necessary data outputting, post-solution
Preamble Elements
Claims 1 and 9: “A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing” MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea
Claims 7 and 10: “A computer-implemented information processing method” MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea
Claims 8 and 11: “An information processing comprising: a memory; and a processor coupled to the memory, the processor being configured to perform processing” device [sic] MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea
Balance of the claim concerns general computer components (preamble) to perform the steps of acquiring and outputting (pre- and post-solution activities), and in the case of claims 9-11, the steps of training and generating. In totality, none of these limitations specify what a “certain field” concretely is to suggest practical application, nor how it relates to some programs which are recited at a high level of generality since none of them are specifically programmed to perform a particular task in real world terms. At best, training is to “enable generation” without informing the reader of how it is enabled, thus simply amounts to apply-it using established functions and does not impose meaningfully limits on the claim. As such, the claims remain drawn to the judicial exception and these additional elements fail to integrate the abstract idea into a practical application.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No—the claims do not include additional elements that amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea in to a practical application, the additional elements are identified with respect to MPEP 2106.05 and do not reveal an inventive concept. In particular, the additional elements are as follows:
Claims 1 and 7-8
“acquiring features that represent contents of each of programs of a program group related to a certain field” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. mere data gathering as a pre-solution step. Particularly, said extra-solution activity is a well-understood, routine and conventional activity per MPEP 2106.05(d)(II)(i)(v) receiving or extracting data. Merely acquiring feature content does not meaningfully limit the claim to demonstrate an inventive concept.
“outputting the one or more of programs” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. necessary data outputting, post-solution. Particularly, said extra-solution activity is a well-understood, routine and conventional activity per MPEP 2106.05(d)(II)(iv) retrieving information in memory. The outputting is recited at a high level of generality and considered fundamental in the computing field.
Claims 9-11
“acquiring a first program related to a certain field” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. mere data gathering or selecting a particular data source or type of data to be manipulated. Particularly, said extra-solution activity is a well-understood, routine and conventional activity per MPEP 2106.05(d)(II)(i)(iii) receiving data or electronic recordkeeping. This further relates to MPEP 2106.05(f)(2)(v) “Requiring the use of software to tailor information and provide it to the user on a generic computer.” Merely acquiring a program does not meaningfully limit the claim to demonstrate an inventive concept.
“training, on the basis of the acquired first program, a first model that enables generation of a new program related to the field according to data features related to the field” MPEP 2106.05(f)(g) adding the words ‘apply it’ (or an equivalent) with the judicial exception and/or adding insignificant extra-solution activity to the judicial exception. For example, this can be a well-understood, routine and conventional activity per instant specification [0030] which describes evidence Cambronero “AL: Autogenerating Supervised Learning Programs” at [P.1 ¶1] “Supervised learning has now become mainstream… widely applied” and/or alternative evidence Lu et al., “CodeXGLUE” arXiv: 2102.04664v2 [Sect3.8 ¶1] “widely used code generation dataset… contains 100,000 examples for training” Fig 6.
“generating a second program related to the field by using the trained first model on the basis of the data features related to the field” MPEP 2106.05(f)(g) adding the words ‘apply it’ (or an equivalent) with the judicial exception and/or adding insignificant extra-solution activity to the judicial exception. Particularly, this is considered a well-understood, routine and conventional activity per specification [0030] which describes evidence Cambronero “AL: Autogenerating Supervised Learning Programs” at [P.1 ¶1] “Supervised learning has now become mainstream… widely applied” where [P.10 Sect.6] “supervised learning programs crowdsourced through Kaggle” and/or GitHub per Lu et al., “CodeXGLUE” arXiv: 2102.04664v2 at [Sect3.8 ¶1] “widely used code generation” showing CodeGPT Fig 6.
“outputting the one or more of programs” MPEP 2106.05(g) adding insignificant extra-solution activity to the judicial exception, e.g. necessary data outputting, post-solution. Particularly, said extra-solution activity is a well-understood, routine and conventional activity per MPEP 2106.05(d)(II)(iv) retrieving information in memory. The outputting is recited at a high level of generality and considered fundamental in the computing field.
Preamble Elements
Claims 1 and 9: “A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing” MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Specifically, these additional elements do not qualify as a particular machine under MPEP 2106.05(b).
Claims 7 and 10: “A computer-implemented information processing method” MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Specifically, these additional elements do not qualify as a particular machine under MPEP 2106.05(b).
Claims 8 and 11: “An information processing comprising: a memory; and a processor coupled to the memory, the processor being configured to perform processing” device [sic] MPEP 2106.05(f) mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Specifically, these additional elements do not qualify as a particular machine under MPEP 2106.05(b).
Significantly more is not established by the balance of the claim for at least the above reasons. The additional elements provide general computer implementation for the computer functions of acquiring and outputting, with claims 7-9 further comprising training and generating. Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. If the claim language provides only a result-oriented solution, with insufficient detail for how a computer accomplishes it, then the claims do contain an inventive concept. Therefore, the claims are found to be patent ineligible. This rejection applies to independent claims 1 and 7-11 as well as to dependent claims 2-6.
Dependent claims when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to the abstract idea, or that they include additional elements which integrate the judicial exception into a practical application or amount to significantly more.
Dependent claim 2 discloses wherein acquiring of features includes contents of programs related to plurality of different fields, and wherein acquiring of performance value represents each of the aforementioned. The limitation of acquiring performance values is considered part of the abstract idea being a mental evaluation such as empirical performance analysis. The limitation of acquiring feature contents is considered additional elements which amount to adding insignificant extra-solution activity under MPEP 2106.05(g) mere data gathering. Particularly, said extra-solution activity is a well-understood, routine and conventional activity per MPEP 2106.05(d)(II)(i)(v) receiving or extracting data. Merely acquiring feature content from various hypothetical fields does not specify what any of the fields are in particular, but simply conveys a plurality that are somehow different. Since a heterogeneous mix of programs may perform different tasks, then the claim is again abstracted because it leaves the reader to value performance without knowing what is performed much less how it is valued, measured or otherwise acquired. The additional elements do not satisfy the test of particular transformation nor meaningfully limit the claim to demonstrate an inventive concept. Accordingly, claim remains drawn to the abstract idea and additional elements do not integrate the abstract idea into a practical application or amount to significantly more.
Dependent claim 3 discloses limitations acquiring, training and generating as already addressed in claim 9, as well as limitation of setting the first and second programs in the group related to a field. The additional limitation of setting is considered part of the abstract idea being mental process of judgment or evaluation. For example, matching a program to a relevant field may be manually judged by an analyst or developer as part of the abstract idea. There are no additional elements.
Dependent claim 4 discloses training a second model that enables generation of a program related to a target field according to data features. The limitation is considered additional elements that amounts to adding the words ‘apply it’ (or an equivalent) with the judicial exception and/or adding insignificant extra-solution activity to the judicial exception per MPEP 2106.05(f)(g). The apply-it training could be an off-the-shelf ensemble or meta-learning with a second model that also enables generation of a program similar to that which is already noted with the exception of a target field. The target has no clear context. As an extra-solution activity, this can be a well-understood, routine and conventional activity per instant specification [0032] reference to Feurer whom describes Auto-sklearn [Sect3.2 ¶2] “It is well known that ensembles often outperform individual models” or alternative evidence Du arXiv: 2107.04773v2 at Fig 3 showing such functionality. In view of the foregoing, no inventive concept is apparent and the additional elements fail to integrate the judicial exception into a practical application or amount to significantly more.
Dependent claim 5 discloses further specifying a number of clusters and wherein selecting from each cluster a number of programs. The limitations are considered part of the abstract idea being mental processes to include judgment. Manually specifying clusters can be performed by a developer and selection of programs is a user choice. To point, k-means clustering is known for this per Tian et al., “Checking Patch Behaviour against Test Specification” at [P.9 Sect4.3 ¶1] “K-means [3] generally provides a good clustering performance and strong interpretability. Its main limitation, however, is that it requires the user to specify the number of clusters” not only supports this as a manual user performed task, but also points to its deficiency. There are no additional elements.
Dependent claim 6 discloses wherein selecting a number of programs whose performance values are relatively great and equal to or greater than a threshold. This is considered part of the abstract idea being mental process of evaluation. For example, top-3 programmes satisfying a ranking or ‘ ≥ ’ minimum quality of performance for performing X relative to a comparative standard. There are no additional elements.
Taken alone, their additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5 and 7-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by:
Ogawa et al., US PG Pub No 2022/0327210A1 hereinafter Ogawa (NEC, Tokyo).
With respect to claim 1, Ogawa teaches:
A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing {Ogawa [0010] “A non-transitory computer readable medium storing a learning program… causes a computer to execute”}, the processing comprising:
acquiring features that represent contents of each of programs of a program group related to a certain field {Ogawa [0062-63] “extracts the feature amounts of the existing malware programs” see Fig 7:S302 feature extraction is acquiring features, malware programs are grouped into clusters [0065], the contents comprise files [0064], and a certain field is malware or detection thereof [0001-05, 29]};
acquiring a performance value that represents performance of each of the programs {Ogawa [0078-79] “a possibility (probability) that the file may be determined to be malware or a normal file from the distance” as “similarity… accuracy of malware” accuracy and/or distance-similarity are performance measures of the malware programs, described e.g. [0069-70] “programs based on a predetermined standard… calculates the similarity” similar at [0061, 64-65] Fig 7:S303};
classifying the program group into a plurality of clusters on the basis of the acquired features {Ogawa [0065] “classifies the existing malware programs into clusters… classified clusters” cont’d “classification element may be a part of feature data elements in the feature amount” shown Fig 7:S304,S302};
selecting, from each cluster of the plurality of clusters, one or more of programs that have relatively great acquired performance values {Ogawa [0069] “select new malware programs for learning from the prepared new malware programs based on a predetermined standard” similar [0061], the predetermined standard as accuracy and/or distance-similarity [0078-79], [0069-70]. Furthermore, Fig 5:122 shows cluster memory unit for accessing “each cluster” [0053,72]}; and
outputting the one or more of programs {Ogawa [0082] “program may be provided to a computer” e.g. output via “malware memory” Fig 4:302 [0039,45], and/or Fig 4:230 output unit in communication with a classification unit [0057,72]}.
With respect to claim 5, Ogawa teaches the non-transitory computer-readable recording medium according to claim 1, the processing further comprising:
specifying a predetermined number of clusters in which programs that have relatively great acquired performance values are classified among the plurality of clusters {Ogawa discloses [0068-67] “the number of clusters is 5… the number of clusters is 2” such that “The cluster size is the number of malware programs in the cluster” among “classified clusters” [0065,70] and performance regarding accuracy and/or distance-similarity as a predetermined standard [0078-79], [0069-70]},
wherein the selecting includes selecting, from each cluster of the specified predetermined number of clusters, a predetermined number of programs that have relatively great acquired performance values {Ogawa [0051-52] “selected new malware programs… according to the number of classified new malware programs” the number of programs may employ an average cluster size [0067], performance entails accuracy and/or distance-similarity as a predetermined standard [0078-89, 69-70]. See similar [0061,69] selecting programs}.
With respect to claim 7, the rejection of claim 1 is incorporated. The difference in scope being a computer-implemented method that performs the information processing of claim 1. Ogawa discloses [0081-82] “method” being “implemented by a computer” e.g. [0038] “computer apparatus such as a server or a personal computer” for [0047] “clustering method.” The remainder of this claim is rejected for the same rationale as claim 1.
With respect to claim 8, the rejection of claim 1 is incorporated. The difference in scope being an information processing comprising a memory and processor coupled thereto that performs the processing of claim 1. Ogawa discloses [0081] “computer including a CPU, a memory… executing the program stored in the memory apparatus by the CPU” similar at [0043]. The remainder of this claim is rejected for the same rationale as claim 1.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ogawa in view of:
Liu et al., US PG Pub No 2022/0391180A1 hereinafter Liu (IBM).
With respect to claim 2, Ogawa teaches the non-transitory computer-readable recording medium according to claim 1, but does not disclose the following limitations. Liu teaches wherein
the acquiring of the features includes featuring that represent contents of each of programs of a program group related to each field of a plurality of fields different from each other is acquired {Liu [0050] “features include… application field, which can be used to classify the projects into different categories such as ‘Java->NLP project’ and ‘Java->Java Web project’. Next, based on the tags and attributes of each projection, an existing clustering algorithm (e.g., DBSCAN [sic]) can be used to classify the projects under each category” the projects are “software projects” [0023-24] i.e. programs from a [0030] “searchable code repository can be utilized such as, but not limited to, GitHub” See also [0045,67] feature selection, illustratively Figs 9-11 and 1-4}, and
the acquiring of the performance value includes acquiring a performance value that represents performance of each of the programs of the program group related to each of the fields {Liu Fig 11 Score of clustered software projects, similarly Fig 9 the score is performance value, so as for [0023] “evaluating code snippets for use by computer programmers when they are generating new software code” e.g. [0066-67] “Project 1 902 which contains the highest scoring logical code block, Project 2 904 which contains the second highest scoring logical code block, and Project 3 906 which includes the third highest scoring logical code block”}.
Liu is directed to clustering for software programs thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to acquire features related to different fields and evaluate by scoring per Liu in combination to arrive at the invention as claimed for the motivation that “the present invention not only help users to get the most relevant code snippet reference from the potentially tens of thousands of projects in a code platform, but they also provide users with a highly rated code snippet” and/or “make it possible to locate the best code snippet reference by mapping the high scoring code snippets with the user intention” (Liu [0025]).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Ogawa in view of:
Parvez et al., “Retrieval Augmented Code Generation and Summarization” hereinafter Parvez (arXiv: 2108.11601v2), and further in view of
Subramanian Rajalakshmi et al., US PG Pub No 2021/0390037A1 hereinafter SR.
With respect to claim 3, Ogawa teaches the non-transitory computer-readable recording medium according to claim 1, the processing further comprising:
acquiring a first program related to the field {Ogawa [0034] “first malware programs collected” collecting is acquiring, program relates to field of malware and detection thereof [0002-05]};
However, Ogawa does not disclose the following limitations which are taught by Parvez:
training, on the basis of the acquired first program, a first model that enables generation of a new program related to the field according to data features related to the field {Parvez Fig 4 Training for code generation task, full framework shown Fig 1 where model comprises encoder-decoder and programs are source code retrieved from repositories GitHub or Stack Overflow. The model training is detailed [P.4,3 Sect.3] and introduced [P.3 ¶1] where input sequence are features and the related field is software development};
generating a second program related to the field by using the trained first model on the basis of the data features related to the field {Parvez Figs 1-2 right, Code Generator where Target Code is the second program and trained model comprises encoder-decoder and features are sequence of inputs as described [P.3 ¶1, Last¶] and model training detailed [P.3-4 Sect.3] Figs 4-5 }; and
Parvez is directed to code generation for development of software as a tool for programmers thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to perform training and generating per Parvez in combination for the motivation “automating source code generation… increasing programmers’ productivity and reducing developers’ tedious workload” code generation being a stated goal (Parvez [P.1 ¶1,3], [P.2 Last¶]). Further, the trained model is shown to outperform the likes of CodeGPT, CodeBERT etc (Parvez [P.6 Tbls.2-4]).
However, the combination Ogawa and Parvez does not disclose the following limitation which is taught by SR:
setting the acquired first program and the generated second program in the program group related to the field {SR [0030] “setting entries 306 in the program matrix… iteratively selects programs 120 from the set of programs” e.g. programs P1 & P2 of a group, Fig 4 and particularly Fig 2:212-214}.
SR is directed to program selection for testing software development with machine learning thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to set programs per SR in combination to arrive at the invention as claimed for the motivation “to identify relationships among the set of programs” (SR [0030]) where “relationship information 114 may comprise program identifiers” such as by row/column (SR [0034,29]). This may provide further benefit of “expediting software testing” (SR [0022]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Ogawa in view of:
Du et al., “Is a Single Model Enough? MuCOS: A Multi-Model Ensemble Learning for Semantic Code Search” hereinafter Du (arXiv: 2107.04773v2).
With respect to claim 4, Ogawa teaches the non-transitory computer-readable recording medium according to claim 1, but does not disclose the following limitations. Du teaches the processing further comprising:
training, on the basis of the predetermined number of selected programs, a second model that enables generation of a program related to a target field according to data features related to the target field {Du [P.1 Last¶] “train multiple models… using an ensemble learning” illustrated Fig 3 “ensemble learning… three pre-trained CodeBert models” CodeBert is code generative models to “generate semantic equivalent code snippets” [P.2 Last2¶] shown per Figs 1-2 or 4 program code and “we also select programs” [P.2 ¶2] e.g. such that the “number of training data is 60000” [P.4 ¶1]. Further, [P.4 ¶4] “our individual models can capture the specific feature… Fig. 5 shows the corresponding code snippet for the natural language query ‘get the field label’” where field is target by way of query for its label}.
Du is directed to program selection with code search and classification thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to train by ensemble technique per Du in combination to arrive at the invention as claimed for the motivation “Instead of training one single model for code snippets and queries, we train multiple models which have specific features and combine them, which can help us better capture the diverse meaning from code snippets” (Du [P.5 ¶2]) thereby addressing when “code may have diverse information from different dimensions, such as business logic, specific algorithm, and hardware communication, making it hard for a single code representation module to cover all the perspectives” (Du [P.2 Las2¶]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ogawa in view of SR.
With respect to claim 6, Ogawa teaches the non-transitory computer-readable recording medium according to claim 1, but does not disclose the following limitations. SR teaches wherein
the selecting includes selecting a predetermined number of programs whose acquired performance values are relatively great and equal to or greater than a first threshold {SR Fig 2:208-214 “Select a Program from the Set of Programs… Select Another Program from the Set of Programs” loop over plurality of programs includes 210 identify programs, identified using [0039-43] “threshold value represents a minimum level of impact for identifying programs… threshold value may be user defined” where a level of impact is between programs (e.g. pairwise) to convey performance in software testing, [0042] “programs 120I and 120J have a distance that is greater than the first distance threshold” Fig 4, see cont’d [0044-46] as well as [0034], [0020], [0004]}.
A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to select programs with threshold per SR in combination to arrive at the invention as claimed for the motivation that it “improves resource utilization and the throughput of the underlying computer system… improves software development technology by providing insight about how modifications to a program that will affect other programs. This process reduces the amount of time required to develop a software project by reducing downtime due to troubleshooting errors” (SR [0004], [0020-22]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Parvez in view of Ogawa.
With respect to claim 9, Parvez teaches:
A {Parvez [P.1 Last¶] “process (see Figure 1)” applicable to [P.8 Last¶] “computer science”}
acquiring a first program related to a certain field {Parvez Fig 1-Left [P.3 ¶1] “retrieval database consisting of an extensive collection of source code (e.g., aggregated from GitHub or Stack Overflow)” similar at [P.5 Sect4.1] “retrieval databases contain code and summaries that are curated from real developers’ open sourced repositories on GitHub”};
training, on the basis of the acquired first program, a first model that enables generation of a new program related to the field according to data features related to the field {Parvez Fig 4 Training for code generation task, full framework shown Fig 1 where model comprises encoder-decoder and programs are source code retrieved from repositories GitHub or Stack Overflow. The model training is detailed [P.4,3 Sect.3] and introduced [P.3 ¶1] where input sequence are features and the related field is software development};
generating a second program related to the field by using the trained first model on the basis of the data features related to the field {Parvez Figs 1-2 right, Code Generator where Target Code is the second program and trained model comprises encoder-decoder and features are sequence of inputs as described [P.3 ¶1, Last¶] and model training detailed [P.3-4 Sect.3] Figs 4-5};
acquiring a performance value that represents performance of each of programs of a program group that includes the acquired first program and the generated second program {Parvez [P.9 Tbl.7] lists values from both human and automatic performance metrics, EM is Exact Match and BLEU scores [P.2 Sect.1 Last¶] “code generation from and Exact Match score of 18.6 to 23.4… BLUE-4 score from 18.45 to 22.95”, and [P.8-9 Sect.6] “score (1 to 5) code based on three criteria (i) similarity, and (ii) relevance w.r.t. the target code; (iii) the compilability of the generated code” see also [P.6 Tbls.2-4] and Fig 11 appendix [P.16]};
However, Parvez does not disclose the following limitations which are taught by SR:
A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing, the processing comprising {Ogawa [0010] “A non-transitory computer readable medium storing a learning program… causes a computer to execute”}
selecting, from the program group, one or more of programs that have relatively great acquired performance values {Ogawa [0069] “select new malware programs for learning from the prepared new malware programs based on a predetermined standard” similar [0061], the predetermined standard as accuracy and/or distance-similarity [0078-79], [0069-70]. Further, Fig 5:122 shows cluster memory unit for accessing “each cluster” [0053,72]}; and
outputting the one or more of selected programs {Ogawa [0082] “program may be provided to a computer” e.g. output via “malware memory” Fig 4:302 [0039,45], and/or Fig 4:230 output unit in communication with a classification unit [0057,72] }.
Ogawa is directed trained models and generating features for software programs thus being analogous. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to select programs to be output using non-transitory computer medium as being obvious to try in choosing from a finite number of identified, predictable solutions, with a reasonable expectation of success where choosing is selecting and a predictable solution is identified as clustering by trained model [0041], and because the training/learning serves as a basis for the selecting per [0069] “select new malware programs for learning.” Additionally, the use of computer medium elements for storing programs to be output and executed for processing would be obvious as applying known devices and techniques for computer implementation as would be appreciated by a person of ordinary skill in the art of computer science as a requisite computing environment.
With respect to claim 10, the rejection of claim 9 is incorporated. The difference in scope being an information processing method implemented by a computer to perform processing of claim 9. Parvez discloses [P.3 Sect.3] “Our proposed code generation” as a method Fig 1 as is applicable to [P.8 Last¶] “computer science.” As such, a requisite computer implementation is further made express by Ogawa [0081], [0038]. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify a computer for implementing the teachings of Parvez as applying known devices to known techniques ready for improvement to yield predictable results as providing computer implementation which would be appreciated by the artisan skilled in computer science. The remainder of this claim is rejected for the same rationale as claim 9.
With respect to claim 11, the rejection of claim 9 is incorporated. The difference in scope being an information processing device comprising memory coupled to processor to the perform processing of claim 9. Ogawa discloses [0081] “computer including a CPU, a memory” similar [0043,38]. A person having ordinary skill in the art would have considered it obvious prior to the effective filing date to specify a computer processor and memory per Ogawa for implementing the teachings of Parvez as applying known devices to known techniques ready for improvement to yield predictable results as providing computer implementation as would be appreciated by the artisan skilled in computer science. The remainder of this claim is rejected for the same rationale as claim 9.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wang et al., US PG Pub No 2022/0382527A1 corresponds to arXiv: 2109.00859v1 discloses CodeT5 similar to CodeGPT, CodeBERT, IntelliCode, CodeXGLUE
Hasan et al., “Text2App: A Framework for Creating Android Apps from Text Descriptions” arXiv: 2104.08301v2 Figs 1-2 MIT App Inventor, input text to output android app
Code clustering patent literature: Zhang US2021/0192321A1, Gottslich US2021/0110308A1
Japan, Usui US2024/0152611A1 Nippon see Fig 4
Examiner example claim: Wang US 11,900,250B2
Conclusion
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/CHASE P. HINCKLEY/Examiner, Art Unit 2124