DETAILED ACTION
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 .
Claims 1-14 have been examined.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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 found in claim 13: “an acquisition part that acquires and stores article data in a memory …; a determination part that calculates an accuracy of a machine learning model …; and a transmission part that transmits, to the one or more articles, control data …”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
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 § 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.
Claim(s) 1-2, 4, 6-10 and 13-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication 20180285772 by Gopalan (“Gopalan”).
Regarding claim 1, Gopalan discloses:
1. An information processing method for an information processing apparatus connected to one or more articles via a communication network, the information processing method comprising: See Gopalan, Fig. 2, broadly depicting a method.
acquiring and storing article data in a memory, the article data including a predetermined data item and being sent from the one or more articles at a predetermined sampling rate; Gopalan, ¶ 0026, “In addition, as referred to herein, a stream may comprise real-time data that is traversing a network or that is being generated by one or more devices, sensors, and so forth. A stream may also comprise a stored series of new data, such as a sequence of images, e.g., discrete images or frames in video, a batch of files, and the like.” Also ¶ 0033, “ In one example, step 230 may relate to at least a portion of the stream of new data, e.g., a data block of a given size, data from a given time window, etc. For instance, step 230 may be repeated with respect to multiple blocks, windows, segments, etc. of the stream of new data, and/or may be repeated with respect to multiple different streams of new data.”
calculating an accuracy of a machine learning model which performs machine learning by using the article data stored in the memory when a data amount of the article data stored in the memory is detected to reach a reference data amount or greater, and Gopalan, ¶ 0038, “ For example, steps 230 and 240 (and optional step 250) may be part of an ongoing process of calculating a likelihood of new data based upon the data distribution of the training data set. For instance, the stream of new data may be segmented into “windows” or blocks, e.g., regular sized blocks, e.g., 100 GB blocks, blocks of data from a unit time interval (e.g., 1 minute files, 5 minute files, etc.), and so forth.” Also ¶ 0044, “In one example, if the accuracy of the machine learning model is less than a desired accuracy, a feature selection process and/or PCA may be re-run on the training data set, or the portion of the training data set used as inputs to the machine learning algorithm to train the machine learning model may be expanded to include additional data, additional labeled examples, and so forth.”
determining at least one of reduction in data item and reduction in sampling rate so that the calculated accuracy satisfies a reference accuracy; and Gopalan, ¶ 0042, “Alternatively, or in addition, step 290 may include decreasing a data sampling rate with respect to at least one of the features with a relevance score that is below a third threshold …”
transmitting, to the one or more articles, control data for controlling the one or more articles to send the article data by using at least one of a data item after the reduction and a sampling rate after the reduction. Gopalan, ¶ 0042, “… or sending an instruction to a network element to decrease a rate of data collection with respect to at least one of the features with a relevance score that is below a third threshold.”
Regarding claim 2, Gopalan also discloses:
2. The information processing method according to claim 1, wherein the predetermined data item includes a plurality of data items, and, Gopalan, ¶ 0031, “For instance, data associated with various features may be randomly sampled, sampled at regular data intervals, and so forth, such that less that all of the available training data set is used to train the machine learning model.”
in the determining, a priority rank of each of the data items is acquired, and one or more candidate data items are determined in descending priority order as remaining data items. Gopalan See Fig. 2 and ¶ 0041, “At optional step 280, the processor may provide an ordered list of the features of the machine learning model ordered by the relevance scores.”
Regarding claim 4, Gopalan also discloses:
4. The information processing method according to claim 2, wherein the priority rank is calculated on the basis of importance of each of the data items calculated in the machine learning of the article data stored in the memory, the machine learning adopting a predetermined machine learning algorithm. Gopalan, Fig. 2 element 280, “ordered by the relevance scores.” Also see e.g. ¶ 0027, “generate a machine learning model, such as a SVM-based classifier, e.g., a binary classifier and/or a linear binary classifier, a multi-class classifier, a kernel-based SVM, etc., a distance-based classifier, e.g., a Euclidean distance-based classifier, or the like, or a non-classifier type machine learning model, such as a decision tree, a KNN predictive model, a neural network, and so forth.”
Regarding claim 6, Gopalan also discloses:
6. The information processing method according to claim 2, wherein each of the candidate data items includes one or more data items combined in the descending priority order. Gopalan, Fig. 2 element 280, “ordered by the relevance scores.”
Regarding claim 7, Gopalan also discloses:
7. The information processing method according to claim 1, further comprising: selecting each of the articles as a first article satisfying a predetermined selection reference or as a second article dissatisfying the predetermined selection reference; and, in the transmitting of the control data, transmitting the control data to the second article without transmitting the control data to the first article. Gopalan, Fig. 2, element 290, “send an instruction to a network element to increase a rate of data collection.” Also see ¶ 0042, “… send an instruction to a network element to increase a rate of data collection or data sampling with respect to the at least one of the features with a relevance score that exceeds the third threshold.”
Regarding claim 8, Gopalan also discloses:
8. The information processing method according to claim 7, wherein, in the selecting, a selection score is calculated for each of the articles on the basis of the article data stored in the memory, and an article having a selection score of a selection reference value or higher is selected as the first article. Gopalan, ¶ 0013, “In one example, features are ranked based upon relevance scores, therefore allowing easy identification of the most impactful features.” Also ¶ 0015, “ For instance, features which have had the greatest increases in relevance score (and, in one example, the greatest decreases in relevance scores) may be listed first, while features which have had little change may appear last in the list.”
Regarding claim 9, Gopalan also discloses:
9. The information processing method according to claim 8, wherein the selection reference value corresponds to a proportion of the first article to the first article and the second article, the proportion being a maximum proportion to allow a learning cost to be a reference learning cost or lower in the machine learning of the article data sent from the first article and the second article. Gopalan, ¶ 0013, “For instance, a purpose of RCA is to identify what features/variables are the root causes that most affect the predictive accuracy of the machine learning model.” Also ¶ 0015, “For instance, features which have had the greatest increases in relevance score (and, in one example, the greatest decreases in relevance scores) may be listed first, while features which have had little change may appear last in the list.”
Regarding claim 10, Gopalan also discloses:
10. The information processing method according to claim 8, wherein the selection score has a value corresponding to a sending frequency of the article data. Gopalan, ¶ 0042, “ At optional step 290, the processor may increase a rate of data sampling and/or a rate of data collection with respect to at least one of the features with a relevance score that exceeds a third threshold, ”
Regarding claim 13, Gopalan discloses:
13. An information processing apparatus connected to one or more articles via a communication network, the information processing apparatus comprising: See Figs. 1 and 3, broadly depicting an apparatus.
All further limitations of claim 13 have been addressed in the rejection of claim 1 above.
Regarding claim 14, Gopalan discloses:
14. A non-transitory computer readable recording medium storing an information processing program for causing a computer to serve as an information processing apparatus connected to one or more articles via a communication network, the information processing program comprising: causing a processor included in the information processing apparatus to execute: See ¶ 0052, “… computer-readable storage device or medium, … More specifically, the computer-readable storage device may comprise any physical devices that provide the ability to store information such as data and/or instructions to be accessed by a processor or a computing device such as a computer or an application server.”
All further limitations of claim 14 have been addressed in the rejection of claim 1 above.
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.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gopalan as applied above, and further in view of U.S. Patent Application Publication 20180336483 by Gross et al. (“Gross”).
Regarding claim 3, Gopalan also discloses:
3. The information processing method according to claim 2, wherein, in the determining, a minimum sampling rate at which the accuracy satisfies the reference accuracy is calculated as a candidate sampling rate for each of the candidate data items, one or more sets each including a candidate sampling rate and a candidate data item corresponding to the candidate sampling rate are generated, and Gopalan, ¶ 0042, “ At optional step 290, the processor may increase a rate of data sampling and/or a rate of data collection with respect to at least one of the features with a relevance score that exceeds a third threshold, … Alternatively, or in addition, step 290 may include decreasing a data sampling rate with respect to at least one of the features with a relevance score that is below a third threshold …”
Gopalan does not expressly disclose: the candidate sampling rate and the candidate data item included in a set having a minimum data amount among the sets are respectively determined as the sampling rate after the reduction and the data item after the reduction. This is taught by Gross. See Gross, ¶ 0058, “Also, the customer does not have to hire a data scientist with a Ph.D. to determine the minimum number of rates and minimum sampling rates for those signals to achieve the desired virtual sensor accuracy.” Also ¶ 0040, “… systematically “weeds out” the least-valuable signals consumed by the NLNP regression technique, and systematically “weeds out” the associated observations, in a manner that preserves accuracy requirements, and attains the lowest possible overall compute cost …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Gross’ minimum data amount and rates with Gopalan’s data in order to preserves accuracy requirements and attain the lowest possible overall compute cost as suggested by Gross.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gopalan as applied above, and further in view of U.S. Patent Application Publication 20200134367 by Chopra et al. (“Chopra”).
Regarding claim 5, Gopalan does not expressly disclose:
5. The information processing method according to claim 4, wherein the machine learning algorithm includes a random forest. This is taught by Chopra. See ¶ 0063, “random forest.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Chopra’s random forest for Gopalan’s algorithm in order to provide for intuitive user visualization as suggested by Chopra (see ¶ 0061).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gopalan as applied above, and further in view of U.S. Patent Application Publication 20200384889 by Nishigaki et al. (“Nishigaki”).
Regarding claim 11, Gopalan does not expressly disclose:
11. The information processing method according to claim 8, wherein the article includes a battery, and the selection score has a value corresponding to at least one of a sending frequency of the article data, a use frequency of the battery, a discharge range of the battery, and an acquisition frequency of an open circuit voltage of the battery. This is taught by Nishigaki. See ¶ 0048, “ The leasable battery information is information about leasable batteries 501 and includes, for example, information such as a state of charge-open circuit voltage (SOC-OCV) characteristic, internal resistance, capacity, hours of use, prices, a new product, and a used product.” It would have been obvious in the data monitoring art to monitor batteries as taught by reference Nishigaki. Using the known technique of battery monitoring to provide the predictable level of article management in Gopalan would have been obvious to one of ordinary skill in the art, since one of ordinary skill in the art would recognize that Gopalan was ready for improvement to incorporate battery monitoring as taught by Nishigaki.
Regarding claim 12, Gopalan does not expressly disclose:
12. The information processing method according to claim 1, wherein the article data includes data about the battery included in the article. This is taught by Nishigaki. See ¶ 0030, “a battery usage state information (vehicle usage state information) representing a usage state of the battery 501 mounted in the vehicle 50 …” It would have been obvious in the data monitoring art to monitor batteries as taught by reference Nishigaki. Using the known technique of battery monitoring to provide the predictable level of article management in Gopalan would have been obvious to one of ordinary skill in the art, since one of ordinary skill in the art would recognize that Gopalan was ready for improvement to incorporate battery monitoring as taught by Nishigaki.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Application Publication 20210271259 by Karpathy et al. (“Karpathy”) See ¶ 0098, “ For example, vehicle control module 709 may be used to change parameters of one or more sensors such as modifying the orientation, changing the output resolution and/or format type, increasing or decreasing the capture rate, adjusting the captured dynamic range, adjusting the focus of a camera, enabling and/or disabling a sensor, etc.”
“Random forest in remote sensing: A review of applications and future directions” by Belgiu et al. See Abstract, “The variable importance (VI) measurement provided by the RF classifier has been extensively exploited in different scenarios, for example to reduce the number of dimensions of hyperspectral data, to identify the most relevant multisource remote sensing and geographic data, and to select the most suitable season to classify particular target classes.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James D Rutten whose telephone number is (571)272-3703. The examiner can normally be reached M-F 9:00-5:30 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James D. Rutten/Primary Examiner, Art Unit 2121
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