Detailed Action
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This is the initial office action based on the application filed on October 14th, 2024, which claims 1-20 have been presented for examination.
Status of Claims
2. Claims 1-20 are pending in the application, of which claims 1, 12 and 19 are in independent form and these claims (1-20) are subject to following rejection(s) and/or objection(s) set forth in the following Office Action below.
Specification
3. The specification is objected to as failing to provide proper antecedent basis for the claimed subject matter. See 37 CFR 1.75(d)(1) and MPEP § 608.01(o).
Correction of the following is required:
Claims 1, 12, and 19 recite limitations i.e. "identify a script for execution by a computing device of an entity"; however, the originally filled disclosure does not provide adequate information regarding this claimed element/term “entity”. The question is what is entity within the scope of this alleged claimed invention? The term “entity” significantly influences or shape up each limitation it appears-in in the claims listed above; however, there is no clear definition in regard to said “entity”. In the specification term/element “entity” has been in very fundamental aspect of the embodiments; however, with very broader scope and without providing sufficient explanation. The rule that a specification need not disclose what is well known in the art is "merely a rule of supplementation, not a substitute for a basic enabling disclosure." Genentech, 108 F.3d at 1366, 42 USPQ2d 1005; see also ALZA Corp., 603 F.3d at 940-41, 94 USPQ2d at 1827. Therefore, the specification must contain the information necessary to enable the novel aspects of the claimed invention. Id. at 941, 94 USPQ2d at 1827; Auto. Technologies, 501 F.3d at 1283-84, 84 USPQ2d at 1115 ("[T]he ‘omission of minor details does not cause a specification to fail to meet the enablement requirement. However, when there is no disclosure of any specific starting material or of any of the conditions under which a process can be carried out, undue experimentation is required.’") (quoting Genentech, 108 F.3d at 1366, 42 USPQ2d at 1005). In order to satisfy the enablement requirement, the specification need not contain an example if the invention is otherwise disclosed in such a manner that one skilled in the art will be able to practice it without an undue amount of experimentation.
Claims 4-5, 8, 10, 15-16 and 18 anticipate similar issues.
Also, although the specification need not teach what is well known in the art, applicant cannot rely on the knowledge of one skilled in the art to supply information that is required to enable the novel aspect of the claimed invention, when the enabling knowledge is in fact not known in the art. The Federal Circuit has stated that “[i]t is the specification, not the knowledge of one skilled in the art, that must supply the novel aspects of an invention in order to constitute adequate enablement” Auto. Technologies, 501 F.3d at 1283, 84 USPQ2d at 1115 (quoting Genentech, Inc. v. Novo Nordisk A/S, 108 F.3d 1361, 1366, 42 USPQ2d 1001, 1005 (Fed. Cir. 1997)).
Therefore, the applicant is suggested to make appropriate amendment to the disclosure to present clear support or antecedent basis for the terms appeared in the said claim; however, no new matter should be introduced.
ALLOWABLE DEPENDENT CLAIM
4. Claim 9 is objected to as being dependent upon respective rejected base claims but would be allowable if rewritten in independent form including all of the limitations of the base claims and any intervening claim(s). However, if the claim 9 is amended; unless necessitated by the other rejections or objections provided in this office action; and/or, any of the currently pending claims are shortened or broaden, and if any new claim(s) are added the office may have right to withdraw the indication of this Allowability provided herewith by this office action.
Claim Rejections – 35 USC §102
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.
5. Claims 1, 3, 6-8, 10-11, 14 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhu et al. (US 8838992 B1 [IDS of record] -herein after Zhu).
Per claim 1:
Zhu discloses:
A system (At least see FIG. 1 with associated text), comprising:
a data processing system comprising one or more processors, coupled with memory (At least see FIG. 1[Wingdings font/0xE0]101 & 108 with associated text), to:
identify a script for execution by a computing device of an entity (At least see Col. 1:53-554 -identifying normal scripts comprises using a machine learning model to determine a classification of a first script in a client computer);
determine, via a model trained with machine learning based on a plurality of scripts established by a plurality of entities (At least see Col.1:30-36 -machine learning model being trained using sample scripts … target script and the web page being received from a server computer over a computer network), a classification of the script prior to execution of the script by the computing device (At least see Col. 1:37-39 -extracted features of the target script are input into the machine learning model to receive a classification of the target scrip); and
control execution of the script responsive to the classification of the script (At least see Col. 4:66-67 -determine whether the script 242 is a normal script or a potentially malicious script (thus, leads to control the execution of the script) [emphasis added]).
Per claim 3:
Zhu discloses:
receive, via a network, the script from a remote device configured to remotely manage the computing device, the script compatible with a plurality of different platforms and configured with a command-line shell (At least see Col. 4:14-19 - training computer 220 may comprise a server computer for building a machine learning model, which in the example of FIG. 2 comprises a support vector machine (SVM) model 225. The training computer 220 may comprise sample scripts 221, a feature set 223, a model generator 224, and the SVM model 225);
determine, responsive to receipt of the script from the remote device and prior to execution of the script on the computing device, the classification of the script (At least see Col. 133-36 - A target script is received in the client computer along with a web page, the target script and the web page being received from a server computer over a computer network); and
control, responsive to the classification, execution of the script to prevent execution of the script or allow execution of the script on the computing device (At least see Col. 5:4-10 a normal script is allowed to run in the client device).
Per claim 6:
Zhu discloses:
scan the script to identify a plurality of values for a plurality of features of the script; and input the plurality of values for the plurality of features into the model to determine the classification (At least see Col. 6:28-33 -- model generator 224 goes through the sample scripts 221 (arrow 301) and extract from the sample scripts 221 attributes that are noted in the feature set 223 (arrow 302). The extracted attributes are also referred to as "features" because the attributes are used as features of feature vectors 324 created for the sample scripts).
Per claim 7:
Zhu discloses:
model is trained with a plurality of features of the plurality of scripts developed by the plurality of entities, the plurality of features comprising one or more features indicative of a coding style, a file attribute, or a code quality (At least see Col. 4:33-39 - attributes in the feature set 223 are also referred to as "features" because they are used to select attributes from a script for use as features in a feature vector. Similarly, attributes selected or extracted from a script are also referred to as "features." Attributes may comprise words or variables that are indicative of normal scripts or potentially malicious scripts).
Per claim 8:
Zhu discloses:
determine, for the entity, a plurality of features established in the model for the entity, the plurality of features comprising at least one of a naming convention, bracket position, maximum line length, trailing whitespace, spare around keywords, style of cmdlet, or indentation (At least see Col. 3:32-39 - lexical analysis of a script may involve determining the number of lines and comments that a script has or whether the script has a well-understood format. Semantic characteristic of a script is the use of meaningful words in the script. For example, semantic analysis of a script may involve determining whether it is possible to relate the appearance of the word "Menu" in the script to some menu operation); and
scan the script to identify a plurality of values for the plurality of features established for the entity (At least see Col. 6:28-33 -- model generator 224 goes through the sample scripts 221 (arrow 301) and extract from the sample scripts 221 attributes that are noted in the feature set 223 (arrow 302). The extracted attributes are also referred to as "features" because the attributes are used as features of feature vectors 324 created for the sample scripts).
Per claim 10:
Zhu discloses:
receive a second plurality of scripts from a third-party repository (At least see Col. 4:44-47 - SVM model 225 and the feature set 223 are subsequently received by the client computer 230 from the training computer 220 (arrow 204). The training computer 220 may be maintained and operated by the vendor of the anti-malware);
train an initial model based on a plurality of features of the second plurality of scripts, the plurality of features comprising one or more features indicative of a coding style, a file attribute, or a code quality (At least see Col. 4:24-31 - model generator 224 may comprise computer-readable program code configured to be executed by the processor of the training computer 220 to build the SVM model 225 by training using a plurality of sample scripts 221 and a feature set 223. In one embodiment, the sample scripts 221 comprise samples of known normal scripts and samples of scripts that are known to be potentially malicious and hence needed to be checked);
receive a third plurality of scripts developed by the entity (At least see Col. 1:34-35 - target script and the web page being received from a server computer over a computer network);
classify, via the initial model, the third plurality of scripts of the entity into a category in the initial model trained based on the second plurality of scripts from the third-party repository (At least see Col. 4:14-17 - training computer 220 may comprise a server computer for building a machine learning model, which in the example of FIG. 2 comprises a support vector machine (SVM) model 225. The training computer 220 may comprise sample scripts); and
train, based on the initial model and the category, the model as a binary classifier to output the classification as one of internal or external (At least see Col. 3:30-35 - script is identified by lexical semantic analysis. Lexical characteristic of a script is structure information of a script. For example, lexical analysis of a script may involve determining the number of lines and comments that a script has or whether the script has a well-understood format).
Per claim 11:
Zhu discloses:
machine learning comprises at least one of a support vector machine, a linear kernel function, or a radial basis kernel function (At least see Col. 7:51-53 - feature extraction process and the feature vector generation process are similar to those in the training stage).
Per claim 12:
Limitations in this independent claim are as similar as claim 1 above; and therefore, rejected based on same rational.
Per claim 14:
Limitations in this dependent claim are as similar as claim 3 above; and therefore, rejected based on same rational.
Per claim 17:
Limitations in this dependent claim are as similar as claim 6 above; and therefore, rejected based on same rational.
Per claim 18:
Limitations in this dependent claim are as similar as claims 10-11 above; and therefore, rejected based on same rational.
Per claim 19:
Limitations in this independent claim are as similar as claim 1 above; and therefore, rejected based on same rational.
Per claim 20:
Limitations in this dependent claim are as similar as claim 8 above; and therefore, rejected based on same rational.
Claim Rejections – 35 USC §103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, 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.
6. Claims 2, 4-5, 13 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhu et al. (US 8838992 B1 [IDS of record] -herein after Zhu) in view of Davydov et al. (US 20170093893 A1 herein after Davydov).
Per claim 2:
Zhu discloses:
determine, via the model and prior to forwarding the script to the computing device, that the classification indicates the script is authorized for execution by the computing device (At least see Col.1:48-51 - allow the script to be used by the web browser without first having the script evaluated by the anti-malware for malicious content in response to detecting that the script is a normal script and not a potentially malicious script); and
forward the script to the computing device for execution responsive to the script being authorized for execution by the computing device (At least see Col. 5:5-8 - SVM model 225, which classifies the script 242 as either a normal script or a potentially malicious script (arrow 207). The normal script identifier 244 allows the script 242 to be employed by the web browser ).
Zhu sufficiently discloses the claimed invention as depicted above, but Zhu does not expressly disclose: wherein the data processing system is intermediary to the computing device and one or more servers, and is further configured to: intercept the script transmitted from the one or more servers to the computing device prior to receipt by the computing device of the script.
However, Davydov discloses:
wherein the data processing system is intermediary to the computing device and one or more servers (At least see [0026] bytecode calculating module as an intermediary between server and client device), and is further configured to:
intercept the script transmitted from the one or more servers to the computing device prior to receipt by the computing device of the script (At least see [0026] - intercept a script requested by the client 101 from the server 100, transmit the intercepted script to the bytecode calculating module 120).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Davydov into Zhu’s invention because Davydov’s teaching would block the execution of malicious scripts requested by a client, which would lead to prevent the running of the intercepted script by intercepting the call for the corresponding API functions; and calculating fuzzy hash sums, where the fuzzy hash sum of data constitutes a set of hash sums calculated from different data regions for which a fuzzy hash sum is calculated; and fuzzy searching, which is a technique of finding an element in a set with the use of data structures (elements of Euclidean space, tree space, and so on), making possible a rapid search for the element of the set nearest to the element being searched, with a small number of comparisons in any given spaces (please see [0027] and [0030]).
Per claim 4:
Zhu sufficiently discloses the claimed invention as depicted above, but Zhu does not expressly disclose: prevent execution of the script by the computing device responsive to the classification comprising an indication that the script was developed by a second entity that is different from the entity, wherein the computing device is managed by the entity.
However, Davydov discloses:
prevent execution of the script by the computing device responsive to the classification comprising an indication that the script was developed by a second entity that is different from the entity, wherein the computing device is managed by the entity (At least see [0023] and [0027] wherein a trusted script developed by a trusted entity are transmitted to client 101 for execution, or in case of otherwise prevented from execution by the client 101).
It would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to incorporate Davydov into Zhu’s invention because Davydov’s teaching would block the execution of malicious scripts requested by a client, which would lead to prevent the running of the intercepted script by intercepting the call for the corresponding API functions; and calculating fuzzy hash sums, where the fuzzy hash sum of data constitutes a set of hash sums calculated from different data regions for which a fuzzy hash sum is calculated; and fuzzy searching, which is a technique of finding an element in a set with the use of data structures (elements of Euclidean space, tree space, and so on), making possible a rapid search for the element of the set nearest to the element being searched, with a small number of comparisons in any given spaces (please see [0027] and [0030]).
Per claim 5:
Zhu sufficiently discloses the claimed invention as depicted above, but Zhu does not expressly disclose: determine, via the model, the classification of the script as one of developed internal to the entity or developed external to the entity.
However, Davydov discloses:
determine, via the model, the classification of the script as one of developed internal to the entity or developed external to the entity (At least see [0023] - trusted script may be a script that does not cause harm to the computer or its user. A trusted script can be considered to be a script developed by a trusted software manufacturer, downloaded from a trusted source (such as a site entered in the library of trusted sites), or a script whose identifier (such as the MD5 of the script file) is stored in a library of trusted scripts. The identifier of the manufacturer, such as a digital certificate, can also be stored in the library of trusted scripts).
Per claim 13:
Limitations in this dependent claim are as similar as claim 2 above; and therefore, rejected based on same rational.
Per claim 15:
Limitations in this dependent claim are as similar as claim 4 above; and therefore, rejected based on same rational.
Per claim 16:
Limitations in this dependent claim are as similar as claim 5 above; and therefore, rejected based on same rational.
CONCLUSION
7. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZIAUL A. CHOWDHURY whose telephone number is (571)270-7750. The examiner can normally be reached on 9:30PM 6:30PM Monday -Friday.
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/ZIAUL A CHOWDHURY/ Primary Examiner, Art Unit 2192
09/04/2026
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