DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 14 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 14 is a substantial duplicate of claim 7. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without any additional elements that provide a practical application or amount to significantly more than the abstract idea.
Independent claim 1, Step 1: method (process = 2019 PEG Step 1 = yes).
Independent claim 1, Step 2A, Prong One: Claim recites:
scanning the histogram buckets and determining when a proportion of the data values assigned to one of the histogram buckets exceeds a threshold value;
and when the proportion of the data values assigned to the one of the histogram buckets exceeds the threshold value
The limitations are substantially drawn to mental concepts: observations, evaluations, judgments, opinions. Information and data also fall within the realm of abstract ideas because information and/or data are intangible. See Electric Power Group1 (Electric Power hereinafter): “Information… is an intangible”.
As to the limitations "scanning the histogram buckets and determining when a proportion of the data values assigned to one of the histogram buckets exceeds a threshold value”, under their broadest reasonable interpretations, they can be characterized as entailing a user analyzing (observations, evaluations) and deciding/determining (judgments, opinions), i.e., processing information and/or data, that can be performed in the human mind or by a human using a pen and paper.
As to the limitations "when the proportion of the data values assigned to the one of the histogram buckets exceeds the threshold value", determinations are mental in nature. These limitations, as drafted and under a broadest reasonable interpretation, can be characterized as entailing a user analyzing/deciding/determining (judgments, opinions), that can be performed in the human mind or by a human using a pen and paper. The specification reads (underline emphasis added):
"[0038] When application 122 determines one or more buckets of histogram 124 exceeds the threshold, application 122 models the data values assigned to that bucket as subsidiary histogram 125 (step 404)"
If a claim limitation, under its broadest reasonable interpretation, covers mental processes, then it falls within the "(c) Mental processes" grouping of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes, (c) Mental processes).
Independent claim 1, Step 2A Prong two: As to the limitations “reading a data record associated with a data pipeline and modeling the data record as a histogram wherein the histogram comprises histogram buckets that categorize data values of the data record”, these limitations describe the concept of “mere data gathering”, which corresponds to the concepts identified as abstract ideas by the courts. Data gathering, including when limited to particular content does not change its character as information, is also within the realm of abstract ideas. Data gathering has not been held by the courts to be enough to qualify as “significantly more”. See Electric Power.
As to the limitations "operating data monitoring system to generate multi-layered histograms" and "modeling the data values assigned to the one of the histogram buckets as a subsidiary histogram wherein the subsidiary histogram comprises subsidiary histogram buckets that categorize the data values assigned to the one of the histogram buckets", they represent no more than just “apply it” limitations, because transformation of information and/or data is not statutory. They invoke computers or other machinery merely as a tool to perform an existing process.
The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial Exception/Abstract idea into a practical application?= NO).
Independent claim 1, Step 2B: As discussed with respect to Step 2A, the claim recites data gathering at a high level of generality; and therefore, these limitations remain insignificant extra-solution activity even upon reconsideration. See MPEP § 2106.05(g).
As discussed with respect to Step 2A, Prong two, the limitations identified as just “apply it” because transformation of information or data is not statutory, information and/or data also fall within the realm of abstract ideas because information and data are intangible. See Electric Power and MPEP 2106.05(f)(2). Transformation of information and/or data is not statutory, because information and data are intangible. These limitations amount to computer implementation of mental concepts including observations, evaluations, judgments, opinions. See for example in the Specification, (underline emphasis added):
'[0058]… Application 122 models the data values that compose file record 111 as a hierarchical histogram (702). For example, application 122 may generate a hierarchical histogram for file record 111'
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
Independent claims 8 and 15, Step 2A Prong One: These claims recite substantially the same elements as claim 1 and are rejected for the same reasons above.
Independent claims 8 and 15, Step 2A Prong two and 2B: As to the further additional elements memory, processor, modeling component configured to, and computer-readable medium, they are interpreted as drawn to a generic computer. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system. The use of a computer to implement the abstract idea of a mathematical algorithm has not been held by the courts to be enough to qualify as “significantly more”. See MPEP 2106.05. The implementation on a computing system is described in the specification (underline emphasis added):
'[0006]… a system to generate multi-layered histograms. The system comprises a memory that stores executable components and a processor. The processor is operatively coupled to the memory and executes the executable components. The executable components comprise a modeling component…
[0068] Processing system 905 may comprise a micro-processor and other circuitry that retrieves and executes software 903 from storage system 902. Processing system 905 may be implemented within a single processing device but may also be distributed across multiple processing devices or sub-systems that cooperate in executing program instructions. Examples of processing system 905 include general purpose central processing units'
Dependent claims, Step 2A, Prong One: Dependent claims limitations further the mental concepts of their independent claims. (See Independent claim 1, Step 2A, Prong One above).
As to the limitations “5/12/19… determining a probability density estimation for the data record based on a distribution of the data values in the histogram and the subsidiary histogram”, under their broadest reasonable interpretation, estimating is a mental concept. Mathematical estimating operations are activities that can be performed in the human mind or by a human using a pen and paper. See for example in the Specification (underline emphasis added):
"[0058]… probability density function indicates a likelihood that a randomly selected data value of file record 111 will possess a given data value. The density function also illustrates the shape of file record 111. Exemplary probability density functions include normal density functions, geometric density functions, exponential density functions, and the like. To estimate the density function, application 122 fits a curve to the hierarchical histogram and calculates a mathematical representation (e.g., a function) for the curve".
As to the limitations "3/10/17… computing a statistical distance between the histogram and the second histogram to determine an amount of difference between the data record and the second data record", they are substantially drawn to mathematical concepts: calculations.
If a claim limitation, under its broadest reasonable interpretation, covers abstract ideas, then it falls within groupings of abstract ideas (2019 PEG Step 2A, Prong One: Abstract Idea Grouping? = Yes).
Dependent claims, Step 2A Prong two: As to the limitations “2/9/16… reading a second data record associated with the data pipeline and modeling the second data record as a second histogram wherein the second histogram comprises second histogram buckets that categorize second data values of the second data record” and "4/11/18… the data record comprises a chronologically first data record; and the second data record comprises a chronologically subsequent data record", these limitations describe the concept of “mere data gathering”. As to the limitations “6/13/20… the data record comprises an output data set generated by the data pipeline”, they further the data gathering of their independent claims. (See Independent claim 1, Step 2A Prong two above).
As to the limitations "2/9/16… modeling the second data values assigned to the one of the second histogram buckets as a second subsidiary histogram wherein the second subsidiary histogram comprises second subsidiary histogram buckets that categorize the second data values assigned to the one of the second histogram buckets", they represent no more than just “apply it” limitations, because transformation of information and/or data is not statutory. They invoke computers or other machinery merely as a tool to perform an existing process.
As to the limitations "3/10/17… applying the amount of difference to a change threshold; when the amount of difference exceeds the change threshold, correlating the amount of difference to a change in the data pipeline and transferring a notification indicating the change" and "5/12/19… generating an output model for the data pipeline based on the probability density estimation", they represent no more than just “apply it” limitations, because they invoke computers or other machinery merely as a tool to perform an existing process.
The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (2019 PEG Step 2A, Prong Two: Additional elements that integrate the Judicial Exception/Abstract idea into a practical application?= NO).
Dependent claims, Step 2B: As discussed with respect to Step 2A, the claims recite data gathering at a high level of generality; and therefore, these limitations remain insignificant extra-solution activity even upon reconsideration. See MPEP § 2106.05(g).
As discussed with respect to Step 2A, Prong two, the limitations identified as just “apply it” because transformation of information or data is not statutory, information and/or data also fall within the realm of abstract ideas because information and data are intangible. (See Independent claim 1, Step 2B above).
As discussed with respect to Step 2A, Prong two, limitations invoking computers or other machinery merely as a tool to perform an existing process are just “apply it” limitations. See MPEP 2106.05(f)(2).
Thus, taken alone the individual additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the additional elements taken individually. There is no indication that their combination improves the functioning of a computer itself or improves any other technology (underline emphasis added). Therefore, the claim does not amount to significantly more than the abstract idea itself (2019 PEG Step 2B: NO).
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Examiner would like to point out that any reference to specific figures, pages, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Hui Wang, (Wang hereinafter), U.S. Patent 9507887, taken in view of Graham Cormode, (Cormode hereinafter), U.S. Patent 8775362.
As to claim 1, Wang discloses a method of operating data monitoring system (see "software… includes performance data monitoring software" in col. 10, lines 3-6) to generate multi-layered histograms (see "using a histogram may select a suitable number of bins or buckets and an interval for each such bin… the size of each bin may be driven by a selected number of bins with each bin having the same size… the score range associated with a bucket interval above the pivot varies so that a reasonable number of data portions are mapped to the associated bucket… the height of the bucket or bin represents the total allocated capacity of the scores mapped to that bin" in col. 41, line 64 to col. 42, line 24), the method comprising: reading a data record associated with a data pipeline (see "service processor 22a may be used in collecting performance data, for example, regarding the I/O performance in connection with data storage system… performance measurements in connection with a data request" in col. 7, lines 61-66) and modeling the data record (see "As each bucket or bin of the histogram has its data portions mapped to the first storage tier, the performance counter (indicating an updated modeled tier RT) is updated to reflect the modeled performance for the first storage tier as also including the additional data portions of the bucket now newly mapped to the first storage tier. For example, as a bucket of data portions is mapped to the first storage tier, the performance or workload information attributed to the newly added data portions in combination with those data portions already mapped to the first storage tier may be input to the appropriate storage tier performance model to determine a modeled aggregate response time" in col. 43, line 63 to col. 44, line 8) as a histogram wherein the histogram comprises histogram buckets that categorize data values of the data record (see "using a histogram may select a suitable number of bins or buckets and an interval for each such bin… the size of each bin may be driven by a selected number of bins with each bin having the same size… the score range associated with a bucket interval above the pivot varies so that a reasonable number of data portions are mapped to the associated bucket… the height of the bucket or bin represents the total allocated capacity of the scores mapped to that bin" in col. 41, line 64 to col. 42, line 24; "processing… where raw scores are mapped into particular buckets involves finding a particular bucket where the raw score falls between the low boundary thereof and the lower boundary of the next bucket" in col. 50, lines 16-21); (see "With… modeling inputs for the aggregated data portions mapped to the first storage tier, the modeling technique may use performance curves to determine an estimated or modeled response time for the physical storage devices in the storage tier based on the aggregate workload of the existing data portions currently mapped to the first storage tier and the additional data portions now also mapped to the first storage tier" in col. 44, lines 14-21); and when the proportion of the data values assigned to the one of the histogram buckets exceeds the threshold value, modeling the data values assigned to the one of the histogram buckets as a subsidiary histogram wherein the subsidiary histogram comprises subsidiary histogram buckets that categorize the data values assigned to the one of the histogram buckets (see "After each bucket of data portions is additionally mapped to the first storage tier to hypothetically represent or model movement of such data portions to the first storage tier, a determination may be made as to whether any of the capacity limits or the response time performance limit for the first tier has been reached or exceeded. If so, the score associated with the current bucket is the promotion threshold… In connection with response time performance modeling for a storage tier… the additional I/Os associated with the data portions being added (via mapping) to a storage pool of a particular storage tier may be modeled as being evenly distributed across drives of the storage pool" in col. 44, lines 23-44; "subsidiary histogram" as "second demotion histogram", "Subsequently a second demotion histogram may be determined using those data portions which have demotion scores from the first histogram less than S1. In other words, those data portions having demotion scores less than S1 are demoted from the EFD storage tier but now a determination may be made as to which storage tier such demoted data portions are located " in col. 47, lines 19).
Wang does not disclose, but Cormode discloses scanning (see “scanning” as "capture", '[t]o generate histogram synopses… divide or separate input probabilistic data into "buckets" so that all tuples falling in the same bucket have similar behavior. Bucket boundaries are selected to minimize a given error function or error metric that measures a within-bucket dissimilarity. In addition… can also generate wavelet synopses to represent probabilistic data by choosing a small number of wavelet basis functions which best describe the data, and contain as much of the "expected energy" of the data as possible. Thus, for both histograms and wavelets, the synopses are generated to capture and describe the probabilistic data as accurately as possible given a fixed size for each synopsis. These synopses can be used to compactly show users the key components of probabilistic data' (see col. 3, lines 31-45).
Wang and Cormode are analogous art because they are related to modeling data.
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Cormode with Wang, because Cormode builds "histogram-based and Haar wavelet-based synopses on probabilistic data (i.e., data associated with probability measures as to the certainty of its accuracy). To generate these synopses, a set or group of histogram bucket boundaries or wavelet coefficients are selected to optimize the accuracy of the approximate representation of a collection of probabilistic tuples under a particular error metric… The histogram or wavelet synopses are constructed or selected by analyzing the structures of the probability distributions and using dynamic programming-based techniques typically used in connection with deterministic domain data" (see col. 3, lines 16-30), and as a result, Cormode reports that '[t]o generate histogram synopses… divide or separate input probabilistic data into "buckets" so that all tuples falling in the same bucket have similar behavior. Bucket boundaries are selected to minimize a given error function or error metric that measures a within-bucket dissimilarity. In addition… can also generate wavelet synopses to represent probabilistic data by choosing a small number of wavelet basis functions which best describe the data, and contain as much of the "expected energy" of the data as possible. Thus, for both histograms and wavelets, the synopses are generated to capture and describe the probabilistic data as accurately as possible given a fixed size for each synopsis. These synopses can be used to compactly show users the key components of probabilistic data' (see col. 3, lines 31-45).
As to claim 2, Wang discloses reading a second data record associated with the data pipeline and modeling the second data record as a second histogram (see "in an ongoing manner at different points in time, collect performance data regarding the external storage tier for different workloads. As the performance characteristics, such as observed RT for a given workload, change, the collected performance data may be updated to include such new information and adapt or adjust the average observed RT for an I/O operation associated with the external storage tier" in col. 66, lines 44-51; "re-evaluation at step 1306 may be performed in response to an occurrence of any suitable event. For example, such re-evaluation may be performed periodically (e.g., upon the occurrence of a predefined time interval), in response to measured or observed system performance reaching a threshold level (e.g., when the measured or monitored response time of the data storage system reaches a defined threshold level), in response to a user's manual selection, and the like" in col. 55, lines 55-63) wherein the second histogram comprises second histogram buckets that categorize second data values of the second data record (see "using a histogram may select a suitable number of bins or buckets and an interval for each such bin… the size of each bin may be driven by a selected number of bins with each bin having the same size… the score range associated with a bucket interval above the pivot varies so that a reasonable number of data portions are mapped to the associated bucket… the height of the bucket or bin represents the total allocated capacity of the scores mapped to that bin" in col. 41, line 64 to col. 42, line 24; "processing… where raw scores are mapped into particular buckets involves finding a particular bucket where the raw score falls between the low boundary thereof and the lower boundary of the next bucket" in col. 50, lines 16-21); (see "With… modeling inputs for the aggregated data portions mapped to the first storage tier, the modeling technique may use performance curves to determine an estimated or modeled response time for the physical storage devices in the storage tier based on the aggregate workload of the existing data portions currently mapped to the first storage tier and the additional data portions now also mapped to the first storage tier" in col. 44, lines 14-21); and when the proportion of the second data values assigned to the one of the second histogram buckets exceeds the threshold value, modeling the second data values assigned to the one of the second histogram buckets as a second subsidiary histogram wherein the second subsidiary histogram comprises second subsidiary histogram buckets that categorize the second data values assigned to the one of the second histogram buckets (see "After each bucket of data portions is additionally mapped to the first storage tier to hypothetically represent or model movement of such data portions to the first storage tier, a determination may be made as to whether any of the capacity limits or the response time performance limit for the first tier has been reached or exceeded. If so, the score associated with the current bucket is the promotion threshold… In connection with response time performance modeling for a storage tier… the additional I/Os associated with the data portions being added (via mapping) to a storage pool of a particular storage tier may be modeled as being evenly distributed across drives of the storage pool" in col. 44, lines 23-44).
Wang does not disclose, but Cormode discloses scanning (see “scanning” as "capture", '[t]o generate histogram synopses… divide or separate input probabilistic data into "buckets" so that all tuples falling in the same bucket have similar behavior. Bucket boundaries are selected to minimize a given error function or error metric that measures a within-bucket dissimilarity. In addition… can also generate wavelet synopses to represent probabilistic data by choosing a small number of wavelet basis functions which best describe the data, and contain as much of the "expected energy" of the data as possible. Thus, for both histograms and wavelets, the synopses are generated to capture and describe the probabilistic data as accurately as possible given a fixed size for each synopsis. These synopses can be used to compactly show users the key components of probabilistic data' (see col. 3, lines 31-45).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Cormode with Wang, (see supra).
As to claim 3, Wang discloses computing a statistical distance between the histogram and the second histogram to determine an amount of difference between the data record and the second data record (see "As each bucket or bin of the histogram has its data portions mapped to the first storage tier, the performance counter (indicating an updated modeled tier RT) is updated to reflect the modeled performance for the first storage tier as also including the additional data portions of the bucket now newly mapped to the first storage tier. For example, as a bucket of data portions is mapped to the first storage tier, the performance or workload information attributed to the newly added data portions in combination with those data portions already mapped to the first storage tier may be input to the appropriate storage tier performance model to determine a modeled aggregate response time" in col. 43, line 63 to col. 44, line 8); applying the amount of difference to a change threshold; when the amount of difference exceeds the change threshold, correlating the amount of difference to a change in the data pipeline and transferring a notification indicating the change (see "in an ongoing manner at different points in time, collect performance data regarding the external storage tier for different workloads. As the performance characteristics, such as observed RT for a given workload, change, the collected performance data may be updated to include such new information and adapt or adjust the average observed RT for an I/O operation associated with the external storage tier" in col. 66, lines 44-51; "re-evaluation at step 1306 may be performed in response to an occurrence of any suitable event. For example, such re-evaluation may be performed periodically (e.g., upon the occurrence of a predefined time interval), in response to measured or observed system performance reaching a threshold level (e.g., when the measured or monitored response time of the data storage system reaches a defined threshold level), in response to a user's manual selection, and the like" in col. 55, lines 55-63).
As to claim 4, Wang discloses wherein: the data record comprises a chronologically first data record; and the second data record comprises a chronologically subsequent data record (see "collecting and tracking activity… Use of the decay coefficients and equations for determining adjusted activity levels to account for previous activity levels provides an effective way of tracking workload and activity over time without having to keep a large database of historical statistics and metrics for long and short time periods" in col. 28, line to col. 29, line 3).
As to claim 5, Cormode discloses determining a probability density estimation for the data record based on a distribution of the data values in the histogram and the subsidiary histogram; and generating an output model for the data pipeline based on the probability density estimation (see "Different models of probabilistic data capture various levels of independence between the individual data values described (i.e., the data items (i)). Each model can be used to describe a distribution over different possible worlds (W). Each possible world (W) is a relation containing some number of tuples (tj). The most general one of the probabilistic models (i.e., the complete model) describes the complete correlations between all tuples (tj)… more compact models are adopted which can reduce the number of parameters by making independence assumptions between tuples… Three probabilistic data models described below include a basic model, a tuple probability density function (pdf) model, and a value pdf model" in col. 4, lines 25-44).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention to use Cormode with Wang, (see supra).
As to claim 6, Wang discloses wherein the data record comprises an output data set generated by the data pipeline (see 'access the performance data, for example, collected for a plurality of LVs when performing a data storage optimization. The performance data 136 may be used in determining a workload for one or more physical devices, logical devices or volumes (LVs) serving as data devices, thin devices (described in more detail elsewhere herein) or other virtually provisioned devices, portions of thin devices, and the like. The workload may also be a measurement or level of “how busy” a device is, for example, in terms of I/O operations (e.g., I/O throughput such as number of I/Os/second, response time (RT), and the like)' in col. 10, lines 33-43).
As to claim 7, Wang discloses wherein the data values comprise numeric data (see "given a bucket I, a raw score will map to bucket I if the raw score has a value between the lower boundary of bucket I and one less than the lower boundary of bucket I+1" in col. 50, lines 11-13).
As to claims 8-20, these claims recite a system comprising a memory and a processor and a computer-readable medium storing instructions for performing the method of claims 1-7. Wang discloses a system (see col. 5, lines 13-14) for performing a method that teaches claims 1-7. Therefore, claims 8-20 are rejected for the same reasons given above.
Conclusion
Examiner would like to point out that any reference to specific figures, pages, columns and lines should not be considered limiting in any way, the entire reference is considered to provide disclosure relating to the claimed invention.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUAN CARLOS OCHOA whose telephone number is (571)272-2625. The examiner can normally be reached Mondays, Tuesdays, Thursdays, and Fridays 9:30AM - 8:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez can be reached at 571-270-1104. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JUAN C OCHOA/Primary Examiner, Art Unit 2186
1 Electric Power Group, LLC v. Alstom S.A., 119 USPQ2d 1739 Fed. Cir. 2016
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