DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 are pending and presented for examination.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The representative claim 15 recites:
A system for or detecting scale in a well production structure, the system comprising:
a computer processor, and memory storing a signal processing engine that comprises instructions that are executable by the computer processor to:
generate a model for the well production structure, the model including a plurality of model parameters that are adjusted based on one or more of:
synthetic data which is not captured by a sensor deployed about the well production structure, and
non-synthetic data which is captured by one or more sensors deployed about the well production structure;
execute a first test using the model to generate first scale data, the first scale data indicating a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of:
an absence of scale within the well production structure, or a presence of substantially minimal scale within the well production structure;
execute a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic data or non-synthetic data to generate a plurality of second scale data;
execute a merging operation between scale data realizations comprised in the second scale data to generate scaling signature data, the merging operation including at least a union of the plurality of second scale data relative to the first scale data;
initiate generation of one or more of: a visualization that indicates a superimposition of the scaling signature data over the first scale data, or an intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category (process).
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and/or mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because the additional limitations in the claim are only: a computer processor, and memory storing a signal processing engine that comprises instructions that are executable by the computer processor…and non-synthetic data which is captured by one or more sensors deployed about the well production structure. The limitation “non-synthetic data which is captured by one or more sensors deployed about the well production structure” is recited at a high level of generality (i.e., gathering or collecting data using sensors) such that it amounts no more than mere instructions to apply the exception using generic sensors.
The limitations “a computer processor, and memory storing a signal processing engine that comprises instructions that are executable by the computer processor” are recited at a high level of generality (i.e., as computer structures performing a generic computer function of processing and storing information) such that they amount no more than mere instructions to apply the exception using generic computer components of processor and memory.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
Claim 15 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the additional elements are recited at a high level of generality (i.e., as generic sensors collecting data and processing and storing the data using a computer components). Further, the additional elements are conventional in the art, as evidenced by the art of record (see, Hernandez de la Bastida et al. US 2023/0399938 (hereinafter, Hernandez), ([0007]-[0008], [0062]), and Biberg et al. WO 2020247595 A1 (hereinafter, Biberg), ([0036], [0113], [0115]). Therefore, claim 15 is directed to an abstract idea without significantly more.
The claim is not patent eligible.
Dependent claims 2-3, 6-11, 13-14, and 18-20, add further details of the identified abstract idea. The claims are not patent eligible.
Dependent claims 4 and 16, recite additional elements of “wherein the scale comprises an accumulation of one or more organic or inorganic materials within a production tubing comprised in the well production structure, the scale impacting the well production structure by: clogging the production tubing and thereby decreasing a rate of fluid production associated with the well production structure; or decreasing an inner diameter of the production tubing and thereby reducing the rate of fluid production associated with the well production structure”. However, these limitations are recited at a high level of generality (i.e., as a scale impacting a well production structure) such that they amount no more than mere accumulation of organic or inorganic materials impacting a well production structure. Further, the additional elements are conventional in the art, as evidenced by the art of record (see, Alhosani et al. “Modeling of asphaltene deposition during oil/gas flow in wellbore”, (hereinafter, Alhosani), (Abstract), and Hernandez ([0049]-[0050]). Therefore, the claims are directed to an abstract idea without significantly more. The claims are not patent eligible.
Dependent claims 5 and 17, recite additional elements of “wherein the one or more sensors deployed about the well production structure comprise at least one of: a multiphase flow sensor; a wellhead pressure sensor; a wellhead temperature sensor; a downhole pressure sensor; a downhole temperature sensor; a casing or tubing pressure sensor; a casing or tubing temperature sensor; and a wellhead flowrate sensor.” However, these limitations are recited at a high level of generality (i.e., gathering or collecting data using sensors) such that they amount no more than mere instructions to apply the exception using generic sensors. Further, the additional elements are conventional in the art, as evidenced by the art of record (see, Hernandez, ([0007]-[0008]), and Biberg ([0036], [0113]). Therefore, the claims are directed to an abstract idea without significantly more. The claims are not patent eligible.
Independent claims 1 and 12, the claims are rejected with the same rationale as in claim 15.
5. Claims 12-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
6. Claim 12 is drawn to “a computer program for detecting scale in a well production structure, the computer program comprising instructions”. Thus, applying the broadest reasonable interpretation in light of the specification and taking into account the meaning of the words in their ordinary usage as they would be understood by one of ordinary skill in the art (MPEP 2111), the claim as a whole does not recite any hardware and does not fall into any of the 4 categories of invention (process, machine, manufacture, or composition of matter).
The Examiner suggests that the Applicant add the limitation “non-transitory computer program” to the claim(s) in order to properly render the claim in statutory form in view of their broadest reasonable interpretation in light of the originally filed specification.
Claim Rejections - 35 USC § 103
7. In the event the determination of the status of the application as subject to AlA 35 U.S.C. 102 and 103 (or as subject to pre-AlA 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.
8. Claims 1-9, 12, 13, and 15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Alhosani et al. “Modeling of asphaltene deposition during oil/gas flow in wellbore”, 2020, Cited in IDS (hereinafter, Alhosani), in view of Hernandez de la Bastida et al. US 2023/0399938 (hereinafter, Hernandez).
9. Regarding claim 1, Alhosani discloses a method for detecting scale in a well production structure, the method comprising:
generating, using a computer processor, a model for the well production structure, the model including a plurality of model parameters that are adjusted (page 7: the detailed framework of the coupled model is provided in Fig. 3. After defining the initial conditions, physical properties, and geometry of the wellbore, the pressure and temperature at the beginning of the next segment is guessed. Then, the properties of the fluid, such as the density and viscosity, are determined based on the average temperature and pressure. The flow pattern is then selected to determine the pressure
and temperature at the end of the segments. If the calculated and guess values are the same, the model moves to the asphaltene deposition calculation, and if the values are not the same, pressure and temperature values are adjusted until convergence is achieved. After that, the deposition model is used to determine the deposition rate of asphaltenes and the thickness of the deposited layer. Finally, the outputs
are used to update the initial conditions);
executing, using the computer processor, a first test using the model to generate first scale data, the first scale data indicating a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of: an absence of scale within the well production structure, or a presence of substantially minimal scale within the well production structure (pages 6-7, section 2.4. Deposition model: After determining the pressure and temperature profiles along the wellbore, the equilibrium concentration of asphaltenes is obtained
using thermodynamic… The asphaltene deposition rate is then calculated…The increase of the concentration of the asphaltene from the wall to the bulk implies that the concentration at the wall is minimal compared to the concentration at the bulk…[Further], page 8, section 3.4: performed to study the physics and mechanisms behind the deposition of asphaltene for annular, dispersed-bubble, and bubble flows. The asphaltene thickness profile is analyzed for different flow patterns,
periods, and gas flow rates…., the asphaltene layer thickness is minimal compared to
bubble flow);
executing, using the computer processor, a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic data or non-synthetic data to generate a plurality of second scale data (pages 6-7: The detailed framework of the coupled model is provided in Fig. 3… the deposition model is used to determine the deposition rate of asphaltenes and the thickness of the deposited layer of a segment N, and determine the deposition rate of asphaltenes and the thickness of the deposited layer of a next segment N + 1 using the deposition model and updated initial condition); and
executing, using the computer processor, a merging operation between scale data realizations comprised in the second scale data to generate scaling signature data, the merging operation including at least a union of the plurality of second scale data relative to the first scale data (pages 6-8, Figs. 3-10: The detailed framework of the coupled model is provided in Fig. 3… the deposition model is used to determine the deposition rate of asphaltenes and the thickness of the deposited layer of a segment N, and determine the deposition rate of asphaltenes and the thickness of the deposited layer of a next segment N + 1 using the deposition model and updated initial condition…and generating an asphaltene thickness profile along the wellbore by combining or merging results of the deposition model).
Alhosani does not disclose:
a plurality of model parameters that are adjusted based on one or more of:
synthetic data which is not captured by a sensor deployed about the well production structure, and non-synthetic data which is captured by one or more sensors deployed about the well production structure.
However, Hernandez discloses:
a plurality of model parameters that are adjusted based on one or more of:
synthetic data which is not captured by a sensor deployed about the well production structure, and non-synthetic data which is captured by one or more sensors deployed about the well production structure (Abstract: receiving real-time data from one or more sensors (i.e., non-synthetic data which is captured by one or more sensors deployed about the well production structure) associated with equipment of a hydrocarbon well production system, predicting scale precipitation in the hydrocarbon well production system based at least in part on the real-time data, and automatically adjusting one or more operating parameters of the equipment based at least in part on the predicted scale precipitation… [0025], [0049]-[0050]: provide scale precipitation predictions including using real-time data throughout the lifetime of a well…the real-time data may include pressure and temperature gathered using sensors installed throughout a production system…[0074]: synthetic data may be generated based on well model simulations, and this synthetic data may be fed to a machine learning algorithm).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alhosani to use a plurality of model parameters that are adjusted based on one or more of:
synthetic data which is not captured by a sensor deployed about the well production structure, and non-synthetic data which is captured by one or more sensors deployed about the well production structure as taught by Hernandez. The motivation for doing so would have been in order to create a real-time scale precipitation prediction system, enabling better surveillance options, and allowing optimum management (Hernandez, [0049]).
10. Regarding claim 2, Alhosani in view of Hernandez disclose the method of claim 1, as disclosed above.
Alhosani further discloses initiating, using the computer processor, generation of one or more of: a visualization that indicates a superimposition of the scaling signature data over the first scale data, or an intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test (pages 8-10, Figs. 4-10: the asphaltene thickness profile along the wellbore is represented). Also, see Hernandez ([0049]-[0050], [0052]).
11. Regarding claim 3, Alhosani in view of Hernandez disclose the method of claim 2, as disclosed above.
Alhosani further discloses wherein the visualization comprises a 3-dimensional visualization that indicates fluid production signatures indicative of a presence or an absence of scale within a production tubing associated with the production structure (pages 9-11, Figs. 7-10: the asphaltene thickness profile along the wellbore is represented as a function of time).
12. Regarding claim 4, Alhosani in view of Hernandez disclose the method of claim 1, as disclosed above.
Alhosani further discloses wherein the scale comprises an accumulation of one or more organic or inorganic materials within a production tubing comprised in the well production structure, the scale impacting the well production structure by: clogging the production tubing and thereby decreasing a rate of fluid production associated with the well production structure; or decreasing an inner diameter of the production tubing and thereby reducing the rate of fluid production associated with the well production structure (Abstract: asphaltene deposition occurs in production lines and leads to a reduction in the production rate due to the contraction of the area. Besides, remedies to treat the deposition costs the industry a high expenditure because of the chemical and the physical removal techniques that may require a partial shutdown of the system. Therefore, it is crucial to accurately predict the rate of deposition to assist in minimizing and controlling this issue,…Pages 9-11, Figs. 7-10: the asphaltene thickness profile along the wellbore can be achieved during several weeks. The thickness of asphaltene increases during the weeks).
13. Regarding claim 16, the claim is rejected with the same rationale as in claim 4.
14. Regarding claim 5, Alhosani in view of Hernandez disclose the method of claim 1, as disclosed above.
Alhosani further discloses wherein the one or more sensors deployed about the well production structure comprise at least one of: a multiphase flow sensor; a wellhead pressure sensor; a wellhead temperature sensor; a downhole pressure sensor; a downhole temperature sensor; a casing or tubing pressure sensor; a casing or tubing temperature sensor; and a wellhead flowrate sensor (Pages 7-8, Figs. 3-6: The model inputs for this graph are given in Table 2. The precipitation rate,.. and initial pressure and temperature). Also, see Hernandez ([0025]).
15. Regarding claim 17, the claim is rejected with the same rationale as in claim 5.
16. Regarding claim 6, Alhosani in view of Hernandez disclose the method of claim 1, as disclosed above.
Alhosani further discloses wherein the first test or the second test comprises a computing simulation that generates that first scale data or the second scale data, respectively (Pages 7-8, Figs. 3-8: the deposition model is for simulation of a deposition process). Also, see Hernandez ([0025], [0074]).
17. Regarding claims 13 and 18, the claims are rejected with the same rationale as in claim 6.
18. Regarding claim 7, Alhosani in view of Hernandez disclose the method of claim 1, as disclosed above.
Alhosani further discloses the first scale data is generated for a first section of the well production structure; and the second scale data is generated for a second section of the well production structure (Pages 7-8, Figs. 3, 5, 7-8: the deposition model can be adopted to the plurality of segments or sections of the wellbore).
19. Regarding claim 8, Alhosani in view of Hernandez disclose the method of claim 6, as disclosed above.
Alhosani further discloses wherein the first scale data and the second scale data are merged to determine scale relationship data between the first scale data and the second scale data (Pages 7-8, Figs. 3-10: the deposition model is used to determine the deposition rate of asphaltenes and the thickness of the deposited layer of a segment N, and determine the deposition rate of asphaltenes and the thickness of the deposited layer of a next segment N + 1 using the deposition model and updated initial condition…The asphaltene thickness profile along the wellbore is represented).
20. Regarding claim 9, Alhosani in view of Hernandez disclose the method of claim 8, as disclosed above.
Alhosani further discloses wherein the scale relationship data is used to determine one of: a union relationship between the first scale data and the second scale data; an intersection between the first scale data and the second scale data; and a complement relationship between the first scale data and the second scale data (Pages 7-8, Figs. 3-10: the deposition model is used to determine the deposition rate of asphaltenes and the thickness of the deposited layer of a segment N, and determine the deposition rate of asphaltenes and the thickness of the deposited layer of a next segment N + 1 using the deposition model and updated initial condition…The asphaltene thickness profile along the wellbore is represented).
21. Regarding claim 12, Alhosani discloses a computer program for detecting scale in a well production structure, the computer program comprising instructions, that when executed by a computer processor of a computing device, causes the computing device to:
generate a model for the well production structure, the model including a plurality of model parameters that are adjusted (page 7: the detailed framework of the coupled model is provided in Fig. 3. After defining the initial conditions, physical properties, and geometry of the wellbore, the pressure and temperature at the beginning of the next segment is guessed. Then, the properties of the fluid, such as the density and viscosity, are determined based on the average temperature and pressure. The flow pattern is then selected to determine the pressure and temperature at the end of the segments. If the calculated and guess values are the same, the model moves to the asphaltene deposition calculation, and if the values are not the same, pressure and temperature values are adjusted until convergence is achieved. After that, the deposition model is used to determine the deposition rate of asphaltenes and the thickness of the deposited layer. Finally, the outputs are used to update the initial conditions);
execute a first test using the model to generate first scale data, the first scale data indicating a normal mode of operation of the well production structure such that the normal mode of operation of the well production structure indicates one of: an absence of scale within the well production structure, or a presence of substantially minimal scale within the well production structure (pages 6-7, section 2.4. Deposition model: After determining the pressure and temperature profiles along the wellbore, the equilibrium concentration of asphaltenes is obtained using thermodynamic… The asphaltene deposition rate is then calculated…The increase of the concentration of the asphaltene from the wall to the bulk implies that the concentration at the wall is minimal compared to the concentration at the bulk…[Further], page 8, section 3.4: performed to study the physics and mechanisms behind the deposition of asphaltene for annular, dispersed-bubble, and bubble flows. The asphaltene thickness profile is analyzed for different flow patterns, periods, and gas flow rates…., the asphaltene layer thickness is minimal compared to bubble flow);
execute a second test based on concurrently adjusting one or more of the plurality of model parameters using updated versions of the synthetic data or non-synthetic data to generate a plurality of second scale data (pages 6-7: The detailed framework of the coupled model is provided in Fig. 3… the deposition model is used to determine the deposition rate of asphaltenes and the thickness of the deposited layer of a segment N, and determine the deposition rate of asphaltenes and the thickness of the deposited layer of a next segment N + 1 using the deposition model and updated initial condition); and
execute a merging operation between scale data realizations comprised in the second scale data to generate scaling signature data, the merging operation including at least a union of the plurality of second scale data relative to the first scale data (pages 6-8, Figs. 3-10: The detailed framework of the coupled model is provided in Fig. 3… the deposition model is used to determine the deposition rate of asphaltenes and the thickness of the deposited layer of a segment N, and determine the deposition rate of asphaltenes and the thickness of the deposited layer of a next segment N + 1 using the deposition model and updated initial condition…and generating an asphaltene thickness profile along the wellbore by combining or merging results of the deposition model);
initiate generation of one or more of: a visualization that indicates a superimposition of the scaling signature data over the first scale data, or an intervention report or an intervention signal for a control operation that mitigates against detected scale in one or more sections of the well production structure based on one or more of the first test or the second test (pages 8-10, Figs. 4-10: the asphaltene thickness profile along the wellbore is represented).
Alhosani does not disclose:
a plurality of model parameters that are adjusted based on one or more of:
synthetic data which is not captured by a sensor deployed about the well production structure, and non-synthetic data which is captured by one or more sensors deployed about the well production structure.
However, Hernandez discloses:
a plurality of model parameters that are adjusted based on one or more of:
synthetic data which is not captured by a sensor deployed about the well production structure, and non-synthetic data which is captured by one or more sensors deployed about the well production structure (Abstract: receiving real-time data from one or more sensors (i.e., non-synthetic data which is captured by one or more sensors deployed about the well production structure) associated with equipment of a hydrocarbon well production system, predicting scale precipitation in the hydrocarbon well production system based at least in part on the real-time data, and automatically adjusting one or more operating parameters of the equipment based at least in part on the predicted scale precipitation… [0025], [0049]-[0050]: provide scale precipitation predictions including using real-time data throughout the lifetime of a well…the real-time data may include pressure and temperature gathered using sensors installed throughout a production system…[0074]: synthetic data may be generated based on well model simulations, and this synthetic data may be fed to a machine learning algorithm).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alhosani to use a plurality of model parameters that are adjusted based on one or more of:
synthetic data which is not captured by a sensor deployed about the well production structure, and non-synthetic data which is captured by one or more sensors deployed about the well production structure as taught by Hernandez. The motivation for doing so would have been in order to create a real-time scale precipitation prediction system, enabling better surveillance options, and allowing optimum management (Hernandez, [0049]).
22. Regarding claim 15, the claim is rejected with the same rationale as in claim 12.
23. Claims 10, 11, 14, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Alhosani, in view of Hernandez, in further view of Biberg et al. WO 2020247595 A1 (hereinafter, Biberg).
24. Regarding claim 10, Alhosani in view of Hernandez disclose the method of claim 1, as disclosed above.
Alhosani further discloses pressure parameter and flow rate parameter (pages 8, and Fig. 6).
Alhosani in view of Hernandez does not disclose:
wherein the model is a 2-phase model comprising: at least a pressure parameter associated with the well production structure; and at least a flow rate parameter associated with the well production structure.
However, Biberg discloses:
wherein the model is a 2-phase model comprising: at least a pressure parameter associated with the well production structure; and at least a flow rate parameter associated with the well production structure ([0040]-[0041], [0090]-[0091]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alhosani in view of Hernandez to use wherein the model is a 2-phase model comprising: at least a pressure parameter associated with the well production structure; and at least a flow rate parameter associated with the well production structure as taught by Biberg. The motivation for doing so would have been in order to analyze and generate scale signature data simply and efficiently (Biberg, [0043]).
25. Regarding claim 19, the claim is rejected with the same rationale as in claim 10.
26. Regarding claim 11, Alhosani in view of Hernandez disclose the method of claim 1, as disclosed above.
Alhosani further discloses pressure parameter, flow rate parameter, and temperature parameter (pages 8, and Fig. 6).
Alhosani in view of Hernandez does not disclose:
wherein the model is a 3-phase model that includes: at least a flow rate parameter associated with the well production structure; at least a pressure parameter associated with the well production structure; and at least a temperature parameter associated with the well production structure.
However, Biberg discloses:
wherein the model is a 3-phase model that includes: at least a flow rate parameter associated with the well production structure; at least a pressure parameter associated with the well production structure; and at least a temperature parameter associated with the well production structure ([0016], [0040]-[0041], [0089]-[0091]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alhosani in view of Hernandez to use wherein the model is a 3-phase model that includes: at least a flow rate parameter associated with the well production structure; at least a pressure parameter associated with the well production structure; and at least a temperature parameter associated with the well production structure as taught by Biberg. The motivation for doing so would have been in order to analyze and generate scale signature data in deeper detail manner (Biberg, [0079]).
27. Regarding claim 20, the claim is rejected with the same rationale as in claim 11.
28. Regarding claim 14, Alhosani in view of Hernandez disclose the computer program of claim 12, as disclosed above.
Alhosani in view of Hernandez does not disclose:
wherein the model is one of a 2-phase model or a 3-phase model,.
However, Biberg discloses:
wherein the model is one of a 2-phase model or a 3-phase model ([0041], [0052]: The basic model to be considered here is generally a two-phase model and/or three phase model).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Alhosani in view of Hernandez to use wherein the model is one of a 2-phase model or a 3-phase model as taught by Biberg. The motivation for doing so would have been in order to analyze and generate scale signature data simply and efficiently (Biberg, [0043]).
Conclusion
29. Examiner has cited particular columns and line numbers, and/or paragraphs, and/or pages in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
30. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EYOB HAGOS whose telephone number is (571)272-3508. The examiner can normally be reached on 8:30-5:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Eyob Hagos/
Primary Examiner, Art Unit 2857
Prosecution Insights