Notice of Pre-AIA or AIA Status
Please vacate the Notice of Allowance mailed September 16, 2025 in light of the attached office action. Then, reset statutory period to response from the mailing date of this office action.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is responsive to the amendment filed on 07/23/2025. Claims 1-6 are pending for examination.
Remarks
The Notice of Allowance mailed September 16, 2025 is withdrawn because claims 3-6 are failing as being indefinite of the 35 U.S.C. 112(b) second paragraph. Examiner apologies for causing any inconvenience.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), 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 3-6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
- Claims 3 and 6 recite term “about” on lines 42-44 and 27, 30 respectively. As described in MPEP 2173.05(b)(III) approximations such as “about” may be indefinite if there is not sufficient context from the specification, prosecution history, or prior art that would indicate what range of activity is covered by the term “about.” In this case, nothing in the specification, prosecution history, or the prior art provides any indication as to what range of specific activity is covered by the term "about.” Therefore, the term “about” appears to render the claim indefinite because the specification lacks any standard for measuring the degrees intended. Applicant is required to make correction.
- Claims 4-5 are rejected for incorporating the deficiency of their respective base claims by dependency.
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, 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 1-2 are rejected under 35 U.S.C. 103 as being unpatentable over Lin et al. ( US 8,868,541), hereinafter “Lin”, in view of Wierman et al., (US 2013/0041881), hereinafter “Wierman”, and further in view of Shah et al., (US 2021/0185066), hereinafter “Shah”.
As per claim 1, Lin discloses a method for reducing memory consumption and increasing bandwidth usage efficiency comprising:
providing a processing logic component (col.5, lines 1-10, col.7, lines 33-37 and col.8, lines 32-64, a crawl request is also issued for the web resource prior to selecting the next web resource, wherein the crawl rate for each web resource is calculated based on the popularity of the web resource such that the estimated crawl interval is inversely proportional to the subscription count of the web resource);
providing a processor (col.10, lines 25-45 and col.5, lines 1-10, a computing system resources (e.g., central processing unit time, memory space, and storage space) coupled to server systems having a web server application for crawling a plurality of resources multiple times to determine a respective change frequency for each resource);
providing a memory operably connected to the processor (col.10, lines 25-45, a computing system resources (e.g., central processing unit time, memory space, and storage space)); and
the memory, including a set of instructions that, when executed causes the processor to perform the steps of (col.10, lines 25-45, server includes hardware or firmware devices including one or more processors, one or more additional devices, a computer readable medium, a communication interface, and, optionally, one or more user interface devices, wherein each processor is capable of processing instructions for execution within the server):
receiving a minimum crawl frequency (col.1, lines 26-46, the first resource will be crawled with a frequency specified by the specific crawling interval or a lesser frequency);
receiving a maximum crawl frequency (col.1, lines 26-46, each group corresponds to a plurality of unique crawling intervals within a time range for the group, and wherein each resource in the plurality resources will be crawled at least as frequently as specified by a maximum crawling interval for the respective group);
receiving a data lower bound value (col.8, lines 45-54, web resources having high subscription counts and change frequencies that are significantly lower than the slowest crawl rates in the assigned bucket are reassigned to a bucket associated with crawl rates corresponding to the change frequency, wherein crawl interval assignments based on popularity are counter balanced by the change frequency of the web resource such that crawl resources utilized by popular web resources having lower change frequencies can be reallocated to less popular web resources that are updated more frequently);
generating a data value prediction, based on a set of historical pricing values, with the recurrent neural network (col.10, lines 11-16, determining the health status of web resources, a change frequency estimator for estimating the frequency of interesting changes to each web resource, and col.1, lines 1-65, crawling the first resource at a crawl frequency less than the frequency specified by the specific crawl interval);
determining, by the processing logic component, a next time to crawl based on the data value prediction (col.1, lines 50-65, determining whether previous crawl attempts were successful, and based on a negative determination, crawling the first resource at a crawl frequency less than the frequency specified by the specific crawl interval);
waiting until the next time to crawl (col.5, lines 58-67, crawl policy for each web resource is determined by crawl scheduler based on, for example, the crawl status of the web resource and an estimated crawl interval);
requesting website data, related to the data value prediction, at the next time to crawl (col.3, lines 4-10, initiating a crawl request to a web crawler resource, a crawl resource scheduler determines the health or accessibility of the resource based upon stored data indicating the success of a prior crawl attempt using the monitoring health of a document, i.e., whether it can be crawled, the popularity of the document, and the frequency of interesting, i.e., substantive, content changes, and using this information to estimate an appropriate crawl interval for each web resource);
receiving the website data (col.6, lines 34-42, a crawl request is issued for the web resource); and
storing the website data (col.6, lines 34-42, storing a corresponding indicator in data storage).
Lin does not explicitly provide a dynamic crawl rate server, connected to a network; and providing a recurrent neural network, resident on the dynamic crawl rate server.
Meanwhile, Wierman discloses providing a dynamic crawl rate server, connected to a network (fig.3. item 320; par. [0006], [0022] and [0044]-[0048], one or more components can reside within a process, and a component can be localized on a computing device (such as a server) or distributed between two or more computing devices communicating across a network, wherein the server is configured to execute a politeness manager that manage web crawlers, wherein the web crawlers are scheduled based on request from web site using crawl rate, the server or politeness manager dynamically change the scheduling of web crawlers, such that differing crawl rates during different time can be generated);
providing a processing logic component, resident on the dynamic crawl rate server (par. [0025], and [0044]-[0048], distributed computing environments where tasks are performed by remote-processing devices that are linked through a communications network, wherein the web crawlers are scheduled based on request from web site using crawl rate, the server or politeness manager dynamically change the scheduling of web crawlers, such that differing crawl rates during different time can be generated),
providing a processor operably connected to the dynamic crawl rate server (par. [0022] and [0044]-[0048], a component may be a process running on a processor, a library, a subroutine, and/or a computer or a combination of software and hardware);
providing a memory operably connected to the processor (par. [0026], processors have memory); and
the memory, including a set of instructions (par. [0019] and [0020], computer-useable instructions embodied on one or more computer-readable media, wherein computer-readable media include both volatile and nonvolatile media, removable and nonremovable media, and contemplates media readable by a database and the various computing devices, application servers, and database servers described herein each may contain different types of computer-readable media to store instructions and data).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Lin to include the features as disclosed by Wierman in order to dynamically change the scheduling of web crawlers, such that differing crawl rates during different time can be generated.
However, neither Lin nor Wierman discloses a neutral network.
On the other hand, Shah discloses a neural network (par. [0157]-[0159], neural network structure for performing multiple passes of a selected i-th application message vector associated with an application message sequence from the training set of application message sequences).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined system of cited references to include the feature as disclosed by Shah to perform multiple passes of a selected i-th application message vector associated with an application message sequence from the training set of application message sequences.
As per claim 2, the combination of Lin, Wierman and Shah discloses the invention as claimed. In addition, Shah discloses providing an input layer having a first set of nodes corresponding to a set of data values (par. [0157], neural network structure of the hidden layers of the VAE includes, wherein a Long Short Term Memory (LSTM) neural network structure for encoding data representing the application message received at the input layer into a form suitable for the VAE);
providing a first LSTM hidden layer connected to the input layer by a first weight matrix (par. [0134] and [0157]-[0159], a feed-forward neural network has a single hidden layer, but more than one uses, in which the corresponding weights of an application weight matrix and a field weight matrix are adjusted by a stochastic gradient descent method using backpropagation techniques);
providing a second LSTM hidden layer connected to the first LSTM hidden layer by a second weight matrix (par. [0139] and [0157]-[0159], the neural network adjusts the corresponding field weights of the field weight matrix, W, and the corresponding application message weights, x.sub.i, of the application weight matrix, X, using backpropagation);
providing a dense hidden layer connected to the second LSTM hidden layer by a third weight matrix (par. [0141] and [0157]-[0159], adjusting the corresponding field weights and application message weights of the field weight matrix, W, and the application weight matrix, X in relation to the i-th selected application message); and
providing an output layer, connected to the dense hidden layer by a fourth weight matrix (par. [0146] and [0157]-[0159], output layer corresponding to the request vector output).
Allowable Subject Matter
Claim 3-6 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) 2nd paragraph rejections set forth in this Office action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Loan T. Nguyen whose telephone number is (571) 270-3103. The examiner can normally be reached on Monday from 10:00 am - 6:00 pm, Thursday-Friday from 10:00 am - 2:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached on (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-270-4103. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
11/22/2025
/LOAN T NGUYEN/Examiner, Art Unit 2165