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 .
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “an obtaining unit, a generation unit, an extraction unit” in claim 1; “a first extraction unit, a second extraction unit” in claim 2.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The recitation of functional “units” in Claims 1 and 2 (“an obtaining unit,” “a generation unit,” and “an extraction unit” in claim 1, and “a first extraction unit” and “a second extraction unit” in claim 2) invoke 35 U.S.C. § 112(f) (formerly sixth paragraph) but the specification does not disclose any corresponding structure, material, or acts for performing those functions, nor does it link any specific structure to the claimed functions. Instead, the specification (e.g., Fig. 14 and paragraphs [0166]-[0177]) merely restates the functions of those units in flow chart without identifying any hardware, software, algorithm, or other means for carrying out the recited functions. Because the written description fails to convey possession of any means or structure that implements the claimed “obtaining,” “generation,” or “extraction” functions, the claims are broader than the inventor’s disclosure and thus are not commensurate with the specification.
Claims 3-7 depend from claim 2 and are rejected for the same reasons.
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.
The following is a quotation of pre-AIA 35 U.S.C. 112, second paragraph::
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7 are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
The limitations “an obtaining unit,” “a generation unit,” and “an extraction unit” in Claim 1, and “a first extraction unit” and “a second extraction unit” in Claim 2 each invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fail to recite any corresponding structural equivalents. Because no structure is set forth in the claims, and because the specification does not identify any specific structure tied to the claimed functions, a person of ordinary skill cannot determine the metes and bounds of the claimed apparatus. As a result, the claim limitations are indefinite for failing to “distinctly claim” the invention.
Claims 3-7 depend from claim 2 and are rejected for the same reasons.
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 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 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
Claims 1, 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Savir et al. (US 2022/0253331, hereinafter Savir) in view of Kakaraparthi (US 2018/0210748, hereinafter Kakaraparthi).
Regarding claim 1, Savir discloses
An information processing apparatus comprising:
an obtaining unit that obtains, for each of a plurality of computers in which software … use history information indicating use histories of the users that use the computers (paragraph [0086]: the use of a special purpose or general-purpose computer including various computer hardware or software modules; paragraph [0072]: collecting telemetry data for each of a plurality of virtual machines (VM), and each of the VMs is associated with a user; collecting usage data for each of the VMs; creating a user profile definition for each user, and the user profile definition is created based on the telemetry data and usage data of the VMs associated with that user);
a generation unit that generates, for each computer, reference use history information indicating a use history serving as a reference for the computer, using the use history information of the users that use the computer (paragraph [0086]: the use of a special purpose or general-purpose computer including various computer hardware or software modules; paragraph [0072]: creating a user profile definition for each user, and the user profile definition is created based on the telemetry data and usage data of the VMs associated with that user … and generating a recommended VM hardware configuration for a VM of one of the users); and
an extraction unit that calculates, for each user that uses the computers, similarities between the use history information of the user (paragraph [0086]: the use of a special purpose or general-purpose computer including various computer hardware or software modules; paragraph [0056]: three different clusters have been defined, based on the respective user profile definitions of the users represented as points in FIG. 3; paragraph [0072]: clustering the users based on similarity of their respective user profiles) and the pieces of reference use history information of the computers (paragraph [0072]: generating a recommended VM hardware configuration for a VM of one of the users), and extracting a computer corresponding to the user based on the similarities (paragraph [0072]: clustering the users based on similarity of their respective user profiles; and generating a recommended VM hardware configuration for a VM of one of the users).
Savir does not disclose each of a plurality of computers in which software that supports a multi-session model and is accessed and used by a plurality of users is installed. Kakaraparthi discloses each of a plurality of computers in which software that supports a multi-session model and is accessed and used by a plurality of users is installed (paragraph [0031]: In order to determine the plurality of sets of common applications, cluster analytics engine 222 extracts similarity patterns and compatibility patterns from the virtual machine management data; paragraph [0033]: luster analytics engine 222 creates a plurality of dedicated virtual desktops and a plurality of session sharable virtual desktops. One of the plurality of sets of common applications is installed on one of the plurality of session sharable virtual desktops and unique applications are installed separately on one of the plurality of dedicated virtual desktops; paragraph [0034]: a single application instance is shared by multiple users and application icons for these shared application instances may be created on user desktops to facilitate session sharing; paragraph [0035]: Once the plurality of dedicated virtual desktops and the plurality of session sharable virtual desktops have been created, placement analytics engine 224 determines similarity patterns and compatibility patterns existing between the plurality of hypervisor hosts, the plurality of dedicated virtual desktops, virtual desktops and the plurality of session shareable virtual desktops on one or more of the plurality of hypervisor host; paragraph [0036]: a virtual desktop provisioning engine 226 that is coupled to data analytics engine 220 re-provisions the plurality of virtual machines based on placement of the plurality of dedicated virtual desktops and the plurality of session shareable virtual desktops on one or more of the plurality of hypervisor hosts, with reduction in per host user density in the virtual desktop infrastructure; paragraph [0039]: the computing device configures each of the plurality of virtual machine agents and each of the one or more hypervisor agents to capture virtual machine management data from the plurality of virtual machines and the one more hypervisor hosts).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Savir’s similarity/selection techniques with Kakaraparthi’s agent-based data capture and host-level aggregation in the multi-session VDI architecture to implement per-computer reference histories and to select VMs by similarity. The motivation would have been to lead reduction in the total resources required by the site users accommodate more virtual desktops in the site without increasing the hypervisor host count, thereby increasing user density served by a hypervisor host, lead to reduction in number of hypervisor hosts and cost optimization due to reduced hardware and software license cost. (Kakaraparth paragraph [0037]).
Regarding claim 8 referring to claim 1, Savir discloses An information processing method that is performed by an information processing apparatus, the method comprising: … (See the rejection for claim 1).
Regarding claim 9 referring to claim 1, Savir discloses A non-transitory computer-readable recording medium that includes a program recorded thereon, the program including instructions that causes a computer to carry out the steps of: … (paragraph [0081]: a computer program product comprising a computer-readable medium).
Claim 2 are rejected under 35 U.S.C. 103 as being unpatentable over Savir in view of Kakaraparthi as applied to claim 1, and further in view of Patel (US 2023/0138741, hereinafter Patel).
Regarding claim 2, Savir discloses
wherein the extraction unit includes:
a first extraction unit that calculates first similarities (paragraph [0056]: three different clusters have been defined, based on the respective user profile definitions of the users represented as points in FIG. 3; paragraph [0072]: clustering the users based on similarity of their respective user profiles) between the reference use history information of each computer and the pieces of use history information of the plurality of users that use the computer (paragraph [0072]: collecting telemetry data for each of a plurality of virtual machines (VM), and each of the VMs is associated with a user; collecting usage data for each of the VMs; creating a user profile definition for each user, and the user profile definition is created based on the telemetry data and usage data of the VMs associated with that user), detects a first similarity … from among the calculated first similarities, and extracts first users corresponding to the detected first similarity (paragraph [0055]: In more detail, having a user profile definition that comprises, or consists of, ‘n’ features, the value of the ‘n’ features can be calculated for each user. In such embodiments, each user may be represented as a point in an ‘n’ dimensional space, and a cluster algorithm, such as k-means for example, can be employed to cluster the users. In general, k-means clustering refers to an unsupervised learning algorithm that partitions ‘n’ objects into k clusters, based on the nearest mean. That is, ‘n’ observations are partitioned into ‘k’ clusters in which each observation belongs to the cluster with the nearest mean), and
a second extraction unit that calculates second similarities (paragraph [0056]: three different clusters have been defined, based on the respective user profile definitions of the users represented as points in FIG. 3; paragraph [0072]: clustering the users based on similarity of their respective user profiles) between the reference use history information of each of the other computers and the pieces of use history information of the extracted first users (paragraph [0072]: collecting telemetry data for each of a plurality of virtual machines (VM), and each of the VMs is associated with a user; collecting usage data for each of the VMs; creating a user profile definition for each user, and the user profile definition is created based on the telemetry data and usage data of the VMs associated with that user), detects a second similarity …, and extracts a second user corresponding to the detected second similarity and a computer corresponding to the second user (paragraph [0055]: In more detail, having a user profile definition that comprises, or consists of, ‘n’ features, the value of the ‘n’ features can be calculated for each user. In such embodiments, each user may be represented as a point in an ‘n’ dimensional space, and a cluster algorithm, such as k-means for example, can be employed to cluster the users. In general, k-means clustering refers to an unsupervised learning algorithm that partitions ‘n’ objects into k clusters, based on the nearest mean. That is, ‘n’ observations are partitioned into ‘k’ clusters in which each observation belongs to the cluster with the nearest mean).
Savir in view of Kakaraparthi does not disclose detects a first similarity that is equal to or smaller than a first threshold value set in advance, from among the calculated first similarities; detects a second similarity that is equal to or larger than a second threshold value set in advance, from among the calculated second similarities.
However, Savir discloses computing similarities between user profiles and clustering users by nearest-mean (k-means) in an n-dimensional feature space (paragraphs [0055]-[0058], [0072]) and Kakaraparthi discloses agent-based capture of per-VM and per-user usage data and host-level aggregation and placement analytics (paragraphs [0022]-[0033], [0035]-[0036]). Savir and Kakaraparthi therefore teach the claimed first and second similarity calculations and the staged extraction of users for cross-host comparison.
Patenl discloses detects a first similarity that is equal to or smaller than a first threshold value set in advance, from among the calculated first similarities; detects a second similarity that is equal to or larger than a second threshold value set in advance, from among the calculated second similarities (paragraph [0069]: Manager system 110 can apply higher than baseline scoring under factor F2 in the case that a K-means clustering Euclidean distance value returned on K-means analysis is less than a threshold indicating strong similarity and can apply less than a baseline scoring value under factor F2 in the case that an aggregate Euclidean distance value returned by the K-means clustering analysis is greater than a threshold indicating strong dissimilarity. Factor F2 comprehends that opinions of users historically more similar to the current user should be weighed more heavily than opinions of other users).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Patel’s applying k-means Euclidean distance thresholds and scoring cutoffs to indicate strong similarity or dissimilarity and weighing historically similar users more heavily to Savir’s applying k-means algorithm for similarity computations operating on the host-aggregated data taught by Kakaraparthi to implement the claimed thresholded first/second similarity detections and extractions, as doing so is a routine parameterization of known clustering and classification techniques.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Savir in view of Kakaraparthi and Patel as applied to claim 2, and further in view of Minarik et al. (US 2023/0052851, hereinafter Minarik).
Regarding claim 3, Savir discloses
wherein the generation unit generates the reference use history information …, using the use histories of the users in a period set in advance (paragraph [0086]: the use of a special purpose or general-purpose computer including various computer hardware or software modules; paragraph [0072]: creating a user profile definition for each user, and the user profile definition is created based on the telemetry data and usage data of the VMs associated with that user … and generating a recommended VM hardware configuration for a VM of one of the users).
Savir in view of Kakaraparthi and Patel does not disclose wherein the generation unit generates the reference use history information by averaging use contents of the users that share the computer using the use histories of the users in a period set in advance. Minarik discloses wherein the generation unit generates the reference use history information by averaging use contents of the users that share the computer using the use histories of the users in a period set in advance (paragraph [0076]: Leveraging the statistical relationship of assets within policies allows a new environment to take advantage of work done by more established environments to quickly setup representative policy groups (e.g., finding that a ‘Production VMs’ policy is typically applied to VM's tagged production and a host of other unique identifiers). These values can be amended as more users adopt these general policies. As an example, if one user creates the base policy that is used as the base for another, and the original policy had a 31-day retention period and the new user changes that retention to 29 days, the new retention variable could be adjusted to 30 days to reflect average usage. Other mathematical formulas such as weighted average and filtering may be applied to the data to account for variations in data).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Savir in view of Kakaraparthi and Patel’s applying k-means algorithm (i.e., k-means Euclidean distance thresholds) for similarity computations operating on the host-aggregated data to applying Minarik’s explicit averaging/weighting rules used by users. The motivation would have been to improve the stability and representativeness of reference histories and to support more accurate similarity-based recommendations.
Regarding claim 4, Savir in view of Kakaraparthi and Patel does not disclose wherein the generating unit detects a use content that occurs on an irregular basis, from the pieces of use history information of the users that share the computer, and replaces the detected use content with a use content implemented by a large number of the users in a time corresponding to the detected use content. Minarik discloses wherein the generating unit detects a use content that occurs on an irregular basis, from the pieces of use history information of the users that share the computer, and replaces the detected use content with a use content implemented by a large number of the users in a time corresponding to the detected use content (paragraph [0076]: Leveraging the statistical relationship of assets within policies allows a new environment to take advantage of work done by more established environments to quickly setup representative policy groups (e.g., finding that a ‘Production VMs’ policy is typically applied to VM's tagged production and a host of other unique identifiers). These values can be amended as more users adopt these general policies. As an example, if one user creates the base policy that is used as the base for another, and the original policy had a 31-day retention period and the new user changes that retention to 29 days, the new retention variable could be adjusted to 30 days to reflect average usage. Other mathematical formulas such as weighted average and filtering may be applied to the data to account for variations in data). Note: Minarik’s teaches detecting irregular or infrequent values in clustered usage data and normalizing or replacing them with values representative of the majority or average use at corresponding times.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine these teachings so that detected irregular use contents (identified by Savir & Patel’s k-means distance/threshold clustering techniques operating on the host-aggregated data of Kakaraparthi) would be normalized or replaced by majority/average values according to Minarik’s taught averaging/weighted-average/filtering methods. The motivation would have been to improve stability and representativeness of per-computer reference histories and yields more reliable similarity-based recommendations.
Allowable Subject Matter
Claims 5-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SISLEY N. KIM whose telephone number is (571)270-7832. The examiner can normally be reached M-F 11:30AM -7:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, April Y. Blair can be reached on (571)270-1014. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SISLEY N KIM/Primary Examiner, Art Unit 2196 01/10/2026