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 .
Response to Amendment
The following is in response to the amendment filed on December 2, 2025.
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 1-3, 6-12 and 15-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim 1”
Step 2A, Prong 1:
A judicial exception is recited in this claim as it recites mental process.
A method for managing machine learning features, the method comprising:
Tracking access, wherein the access are made by a machine learning system to features of a set of features, wherein the tracking generates an access count for each of the individual features; Tracking access and generating a count for each feature could be practically performed in the mind.
Generating ranks for the individual features of the set of features based on the access count, wherein higher rank is associated with more accesses and lower rank is associated with fewer access; and Generating a ranking for the features based on the count could be practically performed in the mind.
Assigning the individual features to different levels of a memory hierarchy based on the rank, wherein higher ranked features are assigned to levels of the memory hierarchy with lower latency and lower ranked features are assigned to levels of the memory hierarchy with lower latency and lower ranked features are assigned to levels of the memory hierarchy with higher latency. Assigning features to different levels of memory based on a ranking could be practically performed in the mind.
Step 2A, Prong 2:
No additional elements are recited.
Step 2B:
No additional elements are recited.
The claim is ineligible.
With respect to claim 2:
Step 2A, Prong 1:
A judicial exception is recited in this claim as it recites a mental process:
Applying a weight to the access count to generate a weighted access count. Applying a weight to generate a weighted access count could be practically performed in the mind.
With respect to claim 3:
Step 2A, Prong 1:
A judicial exception is recited in this claim as it recites a mental process:
Generating the ranks occurs based on the weighted access count. Generating ranks could be practically performed in the mind.
With respect to claim 6:
Step 2A, Prong 1:
A judicial exception is recited in this claim as it recites a mental process:
Generating new features based on the set of features. Generating new features could be practically performed in the mind.
With respect to claim 7:
Step 2A, Prong 1:
A judicial exception is recited in this claim as it recites a mental process:
Filtering the new features. Filtering features could be practically performed in the mind.
With respect to claim 8:
Step 2A, Prong 1:
A judicial exception is recited in this claim as it recites a mental process:
Generating a score from the set of features. Generating a score could be practically performed in the mind.
With respect to claim 9:
Step 2A, Prong 1:
A judicial exception is recited in this claim as it recites a mental process:
Generating comprises performing one or both of crossing and discretization on the set of features. Discretization of a set of features is a mathematical operation.
Claims 10-12, 15-18 and 19-24 are rejected according to claims 1-3 and 6-9.
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.
Claims 1, 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts et al. (US PG Pub 2023/0051103) and Lu et al (CN 111738365).
With respect to claim 1:
Roberts teaches:
A method for managing machine learning features, the method comprising:
Tracking access, wherein the tracking generates an access count for each of the individual features; (Paragraph [044], discloses monitoring access counts for each block of data and generating an access count for each block of data)
Generating ranks for the individual features of the set of features based on the access count, wherein higher rank is associated with more accesses and lower rank is associated with fewer access; and (Paragraph [044], discloses ranking the blocks of data based on the number of accesses, the ranks being cold data or hot data)
Assigning the individual features to different levels of a memory hierarchy based on the rank, wherein higher ranked features are assigned to levels of the memory hierarchy with lower latency and lower ranked features are assigned to levels of the memory hierarchy with lower latency and lower ranked features are assigned to levels of the memory hierarchy with higher latency. (Paragraph [044], discloses assigning data blocks to memory based on their ranking, wherein the hot data (higher ranked) is assigned to lower latency memory and the cold data is assigned to slower memory)
Roberts does not appear to explicitly disclose:
Wherein the access are made by a machine learning system to features of a set of features.
Lu teaches:
Wherein the access are made by a machine learning system to features of a set of features. (Paragraph [010], discloses an image classification model accessing image features from a set features)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the present application to implement a method that utilized the teachings of Roberts and the teachings of Lu, both in the same field of invention. This would allow for data storage optimization while training a model.
Claims 10 and 19 are rejected according to the rejection of claim 1.
Claims 2, 3, 11, 12, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts et al. (US PG Pub 2023/0051103) in view of Lu et al (CN 111738365) and Richardson et al (US Patent 7,783,632).
With respect to claim 2:
The combination of Roberts and Lu does not appear to explicitly disclose:
Applying a weight to the access count to generate a weighted access count.
Richardson teaches:
Applying a weight to the access count to generate a weighted access count. (Column 1 Line 57, “In general, the popularity based ranking of an object can be determined in whole or in part by counting the number of times the object is accessed. The weight of the count can be affected by other factors such as the user's action performed with respect to the object, the rate at which the object is accessed by the same user or by different users, or the user ID or machine ID that accessed the object.” This discloses that the access count weights differently.)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Roberts and Lu and Richardson before them, specifically , to incorporate Richardson’s teachings of ranking objects/features by weights access count. One would have been motivated to make such combination in order to improve the efficiency of ranking objects/features and the importance of each feature/object that is ranked
With respect to claim 3:
Richardson teaches:
Generating the rank occurs based on the weighted access count. (Richardson, Column 2 Line 31, “The various features can be weighted depending on whether a specific ranking is desired. For example, if the user would like to only rank objects accessed in the morning hours, then the training data used to teach the ranking component can rely more heavily on or at least include the time the object is accessed. Thus, the access time feature can be given greater weight than some of the other features. Conversely, if time is less important to this ranking scheme, then the time feature can be weighted less than the other features or given no weight at all.” This teaches that the rankings consider the weightage of each feature’s access count.)
Claims 11, 12 and 20 and 21 are rejected according to the rejections of claim 2 and 3.
Claims 6, 7, 9, 15, 16, 18, 22 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts et al. (US PG Pub 2023/0051103) in view of Lu et al (CN 111738365) and Li et al (CN 113627422).
With respect to claim 6:
The combination of Roberts and Lu does not appear to explicitly disclose:
Generating new features based on the set of features.
Li teaches:
Generating new features based on the set of features. (Page 10, “the model to be trained is used to perform feature fusion processing on the first feature and the second feature to obtain the third feature.” This teaches that a third feature was generated using a set of features that contains first feature and second feature.)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Roberts, Lu and Li before them, specifically , to incorporate Li’s teachings of creating a new feature based on a set of existing features. One would have been motivated to make such combination in order to improve the set of features for better training of a machine learning model.
With respect to claim 7:
Roberts teaches:
Filtering the new features. (Paragraph [049], discloses filtering data blocks)
With respect to claim 9:
Li teaches:
Generating the new features comprises performing one or both of crossing and discretization on the set of features. (Page 10, “the feature fusion processing includes at least one of addition processing, multiplication processing, subtraction processing, cascade processing, and cascade convolution processing.” This discloses that a new feature being generating using multiple process that includes addition(combing) two features.)
Claims 15, 16, 18, 22 and 23 are rejected according to the rejections of claim 6, 7 and 9.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Roberts et al. (US PG Pub 2023/0051103) in view of Lu et al (CN 111738365) and Shaked et al (US Patent 9,805,312)
With respect to claim 8:
The combination of Roberts and Lu does not appear to explicitly disclose:
Generating a score from the set of features.
Shaked teaches:
Generating a score from the set of features. (Column 6 Line 37, “The score for each ranking criterion may be combined to generate a total score for each feature. Accordingly, all the features in a template may be ranked based on the total score generated for each feature.” Column 7 Line 37, “Based on the total scores for each feature shown in Table 1 above, it may be determined that a first set of features exceeds a threshold ranking criteria, e.g., any one or combination of a total score of 1500, a number of occurrences of 950, a number of impressions of 600, and the like.” This discloses that each feature has a score and the combination of all feature in a set of features will have a total score.)
It would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, having the combination of Roberts and Lu before them, specifically, it would have been obvious to score the features of a machine learning model based on its usage or accessed by user. The rationale for this combination lies in improving the efficiency of accessing the important features that are stored in lower memory levels and improving the performances of training a machine learning model with such process.
Claim 18 is rejected according to the rejection of claim 8.
Response to Arguments
Claim Rejections - 35 USC § 101
Applicant argues “Applicants submit that the independent claims of the present application focus on specific improvements to computer technology, and are thus not directed to an abstract idea”
Examiner respectfully disagrees. MPEP 2106.05(a)(II) recites “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” As amended the claim recites assigning features to different levels of a memory hierarchy, this is determined to be an abstract idea because this assignment could be easily performed in the human mind. Examiner would like to point that there is no positive recitation of storing the features in the assigned memory.
Claim Rejections - 35 USC § 103
Applicant’s arguments with respect to the 35 USC 103 rejections have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIELA D REYES whose telephone number is (571)270-1006. The examiner can normally be reached Monday-Friday, 7:30 am -5:00 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, David Wiley can be reached at (571) 272-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142