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 .
DETAILED ACTION
The instant application having Application No. 18059731 has a total of 41 claims pending in the application, of which claims 1-20 and 33 have been cancelled.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 21-32 and 34-41 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-11 of U.S. Patent No. 11551124 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because each of the limitations of the instant claims can be met by limitations of the patent claims as shown below.
As per claim 21,
Instant Application
11551124 B2
Examiners Note
“A computer-implemented method comprising: generating, by one or more processors, forecasted superior domain event data for a hierarchically superior geographic domain at a forecasting period based at least in part on a superior observed data for the hierarchically superior geographic domain at an observation period
Claim 1: determining, based at least in part on observed superior domain input data for a hierarchically superior geographic domain at an observation period and observed superior domain event data for the hierarchically superior geographic domain at the observation period, forecasted superior domain event data for the hierarchically superior geographic domain at a forecasting period,
“Generating, by the one or more processors, an inferior domain event prediction model for a hierarchically inferior geographic domain of the hierarchically superior geographic domain based at least in part on inferior observed data for the hierarchically inferior geographic domain at the observation period”
Claim 1: wherein each hierarchically superior geographic domain is associated with a plurality of hierarchically inferior geographic domains; for each hierarchically inferior geographic domain: generating, based at least in part on observed inferior domain input data for the hierarchically inferior geographic domain at the observation period, an inferior domain event prediction model for the hierarchically inferior geographic domain,
“Generating, by the one or more processors, simulated inferior domain input data for the hierarchically inferior geographic domain at the forecasting period”
Claim 1: determining, based at least in part on the observed inferior domain input data and observed inferior domain event data for the hierarchically inferior geographic domain at the observation period, simulated inferior domain input data for the hierarchically inferior geographic domain at the forecasting period,
“Generating, by the one or more processors, confirmed inferior domain event data for the hierarchically inferior geographic domain at the forecasting period based at least in part on a measure of deviation between the forecasted inferior domain event data and inferred superior domain event data for the hierarchically superior geographic domain”
Claim 1: confirmed inferior domain event data for the hierarchically inferior geographic domain at the forecasting period
Claim 8: determining a measure of deviation between the forecasted superior domain event data and the inferred superior domain event data and determining each confirmed inferior domain event data based at least in part on the measure of the deviation
Generating, by the one or more processors, forecasted inferior domain event data for the hierarchically inferior geographic domain at the forecasting period by processing the simulated inferior domain input data in accordance with the inferior domain event prediction model
Claim 1: and determining, based at least in part on the simulated inferior domain input data and using the inferior domain event prediction model, forecasted inferior domain event data for the hierarchically inferior geographic domain at the forecasting period by processing the simulated inferior domain input data in accordance with the inferior domain event prediction model to generate the forecasted inferior domain event data;
Initiating, by the one or more processors, one or more prediction based actions based at least in part on the forecasted inferior domain event data
Claim 1: performing one or more prediction-based actions based at least in part on the confirmed inferior domain event data.
As can be shown above, each limitation of claim 1 can be met by claims of the 11551124 B2 patent and therefore the claims are rejected as obvious type double patenting.
As per claims 22-32 and 34-41, these claims can be similarly met by claims 1-11 of patent No 11551124 B2, and are rejected for similar reasons given above.
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 21-32 and 34-41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 21 is a process type claim. Claim 39 is a machine type claim, and claim 40 is a manufacture type claim. Therefore, claims 21-40 are directed to either a process, machine, manufacture or composition of matter.
As per claim 21,
2A Prong 1:
“Generating… forecasted superior domain event data for a hierarchically superior geographic domain at a forecasting period based at least in part on superior observed data for the hierarchically superior geographic domain at an observation period” An epidemiologist mentally or with pencil and paper makes a prediction about an epidemic at a state level geographic resolution based on observed data for that region for a certain period of time.
“generating… an inferior domain event prediction model for hierarchically inferior geographic domain of the hierarchically superior geographic domain based at least in part on inferior observed data for the hierarchically inferior geographic domain at the observation period” The epidemiologist mentally or with pencil and paper makes predictions for internal zones of the state level geographic resolution based on data within that region.
“generating… simulated inferior domain input data for the hierarchically inferior geographic domain at the forecasting period” The epidemiology mentally or with pencil and paper takes in data and uses it in their prediction model.
“generating … forecasted inferior domain event data for the hierarchically inferior geographic domain at the forecasting period by processing the simulated inferior domain input data in accordance with the inferior domain event prediction model” The epidemiologist mentally or with pencil and paper calculates results based upon the data.
“generating … confirmed inferior domain event data for the hierarchically inferior geographic domain at the forecasting period based at least in part on a measure of deviation between the forecasted inferior domain event data and inferred superior domain event data for the hierarchically superior geographic domain” The epidemiologist mentally or with pencil and paper compares the internal zones and the state level data to determine differences).
“initiating … one or more prediction based actions based at least in part on the forecasted inferior domain event data” The epidemiologist speaks to an authority figure when an epidemic or other dangerous situation is detected based upon the calculations.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Computer implemented, one or more processors (mere instructions to apply the exception using a generic computer component);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Computer implemented, one or more processors (mere instructions to apply the exception using a generic computer component)
As per claims 22-26, 28-34 and 36-38,and these claims contain additional mental steps similar to claim 21, and are rejected for similar reasons.
As per claim 27, these claims contain additional mental steps to claim 21 and are rejected for similar reasons.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“one or more machine learning models” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic machine learning models with no additional details or limitations that make it more than a generic, off the shelf machine learning model.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“one or more machine learning models” (the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic machine learning models with no additional details or limitations that make it more than a generic, off the shelf machine learning model.
As per claim 35, these claims contain additional mental steps to claim 21 and are rejected for similar reasons.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“generating a cross-geographical event prediction user interface that displays the confirmed inferior domain event data in association with a geographic region placement indication for the hierarchically inferior geographic domain” (Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“generating a cross-geographical event prediction user interface that displays the confirmed inferior domain event data in association with a geographic region placement indication for the hierarchically inferior geographic domain” (MPEP 2106.05(d)(II) indicate that merely “receiving and transmitting data” is a well‐understood, routine, conventional function when it is claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed displaying step is well-understood, routine, conventional activity is supported under Berkheimer).
As per claim 41, these claims contain additional mental steps and generic computer hardware similar to claim 21 and are rejected for similar reasons.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
“one or more machine learning models”, “Wherein the one or more models comprise an ensemble learning model” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic machine learning models with no additional details or limitations that make it more than a generic, off the shelf machine learning model.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
“one or more machine learning models”, “Wherein the one or more models comprise an ensemble learning model” (the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims contain generic machine learning models with no additional details or limitations that make it more than a generic, off the shelf machine learning model.
As per claim 39,
2A Prong 1:
“Generating forecasted superior domain event data for a hierarchically superior geographic domain at a forecasting period based at least in part on superior observed data for the hierarchically superior geographic domain at an observation period” An epidemiologist mentally or with pencil and paper makes a prediction about an epidemic at a state level geographic resolution based on observed data for that region for a certain period of time.
“generating an inferior domain event prediction model for hierarchically inferior geographic domain of the hierarchically superior geographic domain based at least in part on inferior observed data for the hierarchically inferior geographic domain at the observation period” The epidemiologist mentally or with pencil and paper makes predictions for internal zones of the state level geographic resolution based on data within that region.
“generating simulated inferior domain input data for the hierarchically inferior geographic domain at the forecasting period” The epidemiology mentally or with pencil and paper takes in data and uses it in their prediction model.
“generating forecasted inferior domain event data for the hierarchically inferior geographic domain at the forecasting period by processing the simulated inferior domain input data in accordance with the inferior domain event prediction model” The epidemiologist mentally or with pencil and paper calculates results based upon the data.
“initiating one or more prediction based actions based at least in part on the forecasted inferior domain event data” The epidemiologist speaks to an authority figure when an epidemic or other dangerous situation is detected based upon the calculations.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
one or more processors, one or more memories (mere instructions to apply the exception using a generic computer component);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
one or more processors, one or more memories (mere instructions to apply the exception using a generic computer component)
As per claim 40,
2A Prong 1:
“Generating forecasted superior domain event data for a hierarchically superior geographic domain at a forecasting period based at least in part on superior observed data for the hierarchically superior geographic domain at an observation period” An epidemiologist mentally or with pencil and paper makes a prediction about an epidemic at a state level geographic resolution based on observed data for that region for a certain period of time.
“generating an inferior domain event prediction model for hierarchically inferior geographic domain of the hierarchically superior geographic domain based at least in part on inferior observed data for the hierarchically inferior geographic domain at the observation period” The epidemiologist mentally or with pencil and paper makes predictions for internal zones of the state level geographic resolution based on data within that region.
“generating simulated inferior domain input data for the hierarchically inferior geographic domain at the forecasting period” The epidemiology mentally or with pencil and paper takes in data and uses it in their prediction model.
“generating forecasted inferior domain event data for the hierarchically inferior geographic domain at the forecasting period by processing the simulated inferior domain input data in accordance with the inferior domain event prediction model” The epidemiologist mentally or with pencil and paper calculates results based upon the data.
“initiating one or more prediction based actions based at least in part on the forecasted inferior domain event data” The epidemiologist speaks to an authority figure when an epidemic or other dangerous situation is detected based upon the calculations.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
One or more non-transitory computer readable media, one or more processors, (mere instructions to apply the exception using a generic computer component);
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
One or more non-transitory computer readable media, one or more processors, (mere instructions to apply the exception using a generic computer component)
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 21-32, 34-38 and 41 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim 21, this claim calls for “generating by the one or more processors, confirmed inferior domain event data for the hierarchically inferior geographic domain at the forecasting period based at least in part on a measure of deviation between the forecasted inferior domain event data and inferred superior domain event data for the hierarchically superior geographic domain.” However, the specification does not support this limitation. The only discussions of deviations are found in paragraphs 0079, 0082-0083, and 0098-0100. Paragraphs 0079 and 0082-0083 denotes comparisons between simulated superior domain event data and preliminary superior domain event data. None of these paragraphs make any mention of comparing forecasted inferior domain event data to inferred superior domain event data.
Paragraphs 0098-0100 discuss comparisons between the forecasted superior domain event data for the particular superior geographic domain and the inferred superior domain event data for the particular superior geographic domain. While the inferred superior domain event data is created by aggregating forecasted inferior domain event data, there is never a comparison between individual forecasted inferior domain event data and inferred superior domain event data. Therefore this limitation is new matter and therefore rejected under U.S.C. 112(a).
As per claims 22-32, 34-38, and 41, these claims are rejected as being dependent on a claim rejected under U.S.C. 112(a) for new matter.
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.
Claim 41 is 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.
As per claim 41, this claim calls for “using one or more machine learning models” … wherein the one or more machine learning models comprise an ensemble learning model”….” However, an ensemble learning algorithm by its very nature requires multiple machine learning model. A single machine learning model cannot be said to “comprise an ensemble learning model” as an ensemble would require at least more than one. This causes the claim to be confusing and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
As per claim 41, this claim is dependent on claim 1, which has been cancelled. Further the claims refer to “decomposed timeseries distributions” and “observed superior domain input data” which is not in any of the independent claims. Without a proper dependency it is unclear just how this claim is intended to be used and connected to the other claims. This causes the claim to be confusing and therefore rejected under U.S.C. 112(b) for failing to particularly point out and claim the intended invention.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-24, 29-30, 33, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Herz et al (US 20140095417 A1) in view of Lombardo et al (US 20030009239 A1) and Kirby et al (“Advances in spatial epidemiology and geographic information systems”).
As per claims 21 and 39-40, Herz discloses, “A computer-implemented method” (abstract; EN; this denotes a computer system which inherently includes memory, processors, computer code, and the like in order to execute the actions of the system).
“generating, by one or more processors, forecasted … domain event data” (Pg.7, particularly paragraph 0084; EN: this denotes the system predicting the spread of an epidemic). “… for a geographic domain” (Pg.7, particularly paragraph 0085; EN: this denotes the consideration of geographic locations for spreading disease). “at a forecasting period” (Pg.7, particularly paragraph 0083; EN: this denotes the forecast of the spread over time). “based at least in part on …. Observed data for the … geographic domain” (Pg.4, particularly paragraph 0058; EN: this denotes using data associated with the geographic areas). “at an observation period” (pg.4, particularly paragraph 0058; EN: this denotes using the data collected at set intervals).
“generating, by the one or more processors, an … domain event prediction model … based at least in part on… observed data for the … geographic domain at the observation period” (pg.4, particularly paragraph 0059; EN: this denotes using a Bayesian belief network to make predictions).
“generating, by the one or more processors, simulated … domain input data for the … geographic domain at the forecasting period” (Pg.5-6, particularly paragraph 0071; EN: This denotes putting the data into the model, here the data is “simulated” because it is being placed as variables to the model, and are part of a computer system).
“generating, by the one or more processors, forecasted … domain even data for the …. Geographic domain at the forecasting period by processing the simulated … domain input data in accordance with the … domain event prediction model” (pg.5-6, particularly paragraph 0071; EN: this denotes the system predicting potential epidemics based upon the data).
“generating, by the one or more processors, confirmed … domain event data for the … geographic domain at the forecasting period …” (pg.5, particularly paragraph 0061; EN: this denotes improving the accuracy by gathering new data for the system and training it (i.e. confirming that the system is performing correctly by confirming its predictions).
“initiating, by the one or more processors, one or more prediction based actions based at least in part on the forecasted … domain event data” (pg.5-6, particularly paragraph 0071; EN: this denotes the system predicting potential epidemics based upon the data and alerting authorities about the epidemic).
However, Herz fails to explicitly disclose “superior domain” and “for a hierarchically superior geographic domain”, “ Superior”, “hierarchically superior”, “inferior domain”, “a hierarchically inferior geographic domain of the hierarchically superior geographic domain”, “inferior”, “hierarchically inferior geographic domain”, and “based at least in part on a measure of deviation between the forecasted inferior domain event data and inferred superior domain event data for the hierarchically superior geographic domain”
Lombardo discloses, “superior domain”, “for a hierarchically superior geographic domain”, “ Superior”, “hierarchically superior”, “inferior domain”, “a hierarchically inferior geographic domain of the hierarchically superior geographic domain”, “inferior”, “hierarchically inferior geographic domain” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
Kirby discloses, “based at least in part on a measure of deviation between the forecasted inferior domain event data and inferred superior domain event data for the hierarchically superior geographic domain” (pg.5, particularly the Spatial clustering section; EN: this denotes comparing magnitudes (i.e. deviations) between different scales for determining disease over areas. When combined with the Herz and Lombardo reference, this denotes looking at the differences between the areas to find the best way to monitor the situation based on the hierarchy of data).
Herz and Lombardo are analogous art because both involve epidemic prediction.
Before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Lombardo in order to allow predictions to be made at various hierarchical levels.
The motivation for doing so would be to allow the system to “be applied simultaneously over multiple time scales. In particular, depending on the resolution of the data, they can be applied at the neighborhood, district, county, state, or national levels” (Lombardo, Pg.4, paragraph 0060) or in the case of Herz, allow the system to be applied at different geographic scales as needed by the system to predict epidemics in monitored regions.
Therefore before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Lombardo in order to allow predictions to be made at various hierarchical levels.
Kirby and Herz modified by Lombardo are analogous art because both involve epidemiology.
Before the effective filing date it would have been obvious to one skilled in the art of epidemiology to combine the work of Kirby and Herz modified by Lombardo in order to allow comparisons between different scales of epidemiological data.
The motivation for doing so would be to allow the system to “ determine whether disease rates around a hazard site are elevated or if the number of infected individuals is higher than would be expected. Accurate cluster information provides practical knowledge for public health interventions such as screening, prevention, and surveillance” (Kirby, Pg.5, C1, first paragraph) or in the case of Herz modified by Lombardo, allow the system to determine which statistics are the most accurate by comparing magnitude at different scales.
Therefore before the effective filing date it would have been obvious to one skilled in the art of epidemiology to combine the work of Kirby and Herz modified by Lombardo in order to allow comparisons between different scales of epidemiological data.
As per claim 22, Herz discloses, “wherein the … domain event prediction model is configured to generate one or more predicted events based at least in part on prediction input data for an … domain event of the … geographic domain” (Pg.7, particularly paragraph 0083; EN: this denotes the forecast of the spread over time).
Lombardo discloses, “inferior domain” and “hierarchically inferior geographic domain” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
As per claim 23, Herz discloses, “wherein the … observed data for the … geographic domain comprises observed … domain input data for the … geographic domain at the observation period and observed … domain event data for the … geographic domain at the observation period” (pg.5, particularly paragraph 0058; EN: this denotes observed event data and input data used by the system).
Lombardo discloses, “superior”, “hierarchically superior geographic domain”, “(pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
As per claim 24, Herz discloses, “Generating, by the one or more processors, preliminary … domain event data for the … geographic domain at the forecasting period “ (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model).
“generating, by the one or more processors, simulated … domain input data for the … geographic domain at the forecasting period based at least in part on the observed … domain input data” (Pg.4, particularly paragraph 0058; EN: this denotes using data associated with the geographic areas).
“generating, by the one or more processors, the forecasted … domain event data based at least in part on the preliminary … domain event data” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model). “and the simulated … domain input data” (Pg.5-6, particularly paragraph 0071; EN: This denotes putting the data into the model, here the data is “simulated” because it is being placed as variables to the model, and are part of a computer system).
Lombardo discloses, “superior”, “hierarchically superior geographic domain”, “(pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
As per claim 29, Herz discloses, “wherein the simulated … domain input data is generated based at least in part on the … observed data” (Pg.5-6, particularly paragraph 0071; EN: This denotes putting the data into the model, here the data is “simulated” because it is being placed as variables to the model, and are part of a computer system).
Lombardo discloses, “inferior” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
As per claim 30, Herz discloses, “Wherein the … observed data comprises observed … domain input data … at the observation period” (Pg.5-6, particularly paragraph 0071; EN: This denotes putting the data into the model, here the data is “simulated” because it is being placed as variables to the model, and are part of a computer system).
Lombardo discloses, “Inferior” and “hierarchically inferior geographic domain” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
As per claim 33, Herz discloses, “generating, by the one or more processors, confirmed … domain event data for the … geographic domain at the forecasting period based at least in part on the forecasted … domain event data” (pg.5, particularly paragraph 0061; EN: this denotes improving the accuracy by gathering new data for the system and training it (i.e. confirming that the system is performing correctly by confirming its predictions).
Lombardo discloses, “inferior” “hierarchically inferior geographic domain” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
As per claim 37, Herz discloses, “wherein the one or more prediction-based actions are based at least in part on the confirmed … domain event data” (pg.5-6, particularly paragraph 0071; EN: this denotes the system predicting potential epidemics based upon the data).
Lombardo discloses, “inferior” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
Claim Rejections - 35 USC § 103
Claims 25-28 and 41 are rejected under 35 U.S.C. 103 as being unpatentable over Herz et al (US 20140095417 A1) in view of Lombardo et al (US 20030009239 A1) and Kirby et al (“Advances in spatial epidemiology and geographic information systems”) and further in view of Feng et al (“A Unified Framework of Epidemic Spreading Prediction by Empirical Mode Decomposition-Based Ensemble Learning Techniques”).
As per claim 25, Herz discloses, “wherein generating the preliminary … domain event data comprises” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model).
“generating, by the one or more processors, one or more exogenous variables based at least in part on the observed … domain input data” (Pg.5, particularly paragraph 0066; EN: this denotes looking at exogenous variables such as retail sales and pharmacy sale records).
“generating, by the one or more processors, the preliminary … domain event data” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model). based at least in part on … the one or more exogenous variables” (Pg.5, particularly paragraph 0066; EN: this denotes looking at exogenous variables such as retail sales and pharmacy sale records).
Lombardo discloses, “superior” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
However, Herz fails to explicitly disclose, “generating, by the one or more processors, a timeseries distribution based at least in part on the observed … domain event data” and “the time-series distribution.”
Feng discloses, “generating, by the one or more processors, a timeseries distribution based at least in part on the observed … domain event data” and “the time-series distribution” (pg.662, particularly C1, paragraph before section III; EN: this denotes using the Autoregressive (AR) order to the time series, which according to the specification is a type of time-series distribution).
Herz and Feng are analogous art because both involve epidemic prediction.
Before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Feng in order to use time-series distributions for epidemic prediction.
The motivation for doing so would be to allow the system to use “autocorrelation functions (ACF) [to be] utilized to obtain the model order of the weekly consultation rates. Based on the ACF results in Fig. 8, the value of AR order l is chosen….” (Feng, Pg.666, C1, section D) or in the case of Herz, allow the system to use the autoregressive values of the time series data to help calculate spreading predictions of the epidemic as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Feng in order to use time-series distributions for epidemic prediction.
As per claim 26, Herz discloses, “wherein generating the preliminary … domain event data comprises” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model).
“Generating, by the one or more processors, the preliminary … domain event data…” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model).
Lombardo discloses, “superior” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
Feng discloses, “generating, by the one or more processors, a group of decomposed timeseries distributions comprising one or more intrinsic mode function distributions” (pg.661, particularly C1, third paragraph; EN: this denotes creating the IMFs). “and an error distribution” (pg.666, particularly C2, last paragraph; pg.667, C1, first paragraph; EN: this denotes various error distributions) “based at least in part on an empirical mode decomposition of the timeseries distribution” (Pg.662-663, particularly section B EN: This denotes the empirical mode decomposition data).
“… based at least in part on the group of decomposed time series distributions” (Pg.662-663, particularly section B EN: This denotes the empirical mode decomposition data being used for epidemic prediction).
As per claim 27, Herz discloses, “wherein generating the preliminary … domain event data comprises” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model).
“Generating, by the one or more processors, and using one or more machine learning models, one or more per-model preliminary event data objects based at least in part on the observed … domain input data …” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model).
“Generating, by the one or more processors, the preliminary … domain event data based at least in part on the one or more per-model preliminary event data objects” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model).
Lombardo discloses, “superior” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
Feng discloses, “the group of decomposed timeseries distributions” (Pg.662-663, particularly section B EN: This denotes the empirical mode decomposition data being used for epidemic prediction).
As per claim 28, Herz discloses, “wherein a machine learning model of the one or more machine learning models is configured to process the … one or more exogenous variables to generate a particular per-model preliminary event data object” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model. Pg.5, particularly paragraph 0066; EN: this denotes looking at exogenous variables such as retail sales and pharmacy sale records).
Feng discloses, “the group of decomposed timeseries distributions” (Pg.662-663, particularly section B EN: This denotes the empirical mode decomposition data being used for epidemic prediction).
As per claim 41, Herz discloses, “generating, by the one or more processors and using one or more machine learning models” (abstract; EN: this denotes the use of machine learning). “one or more per-model preliminary event data objects based at least in part on the observed … domain input data” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model. Pg.5, particularly paragraph 0066; EN: this denotes looking at exogenous variables such as retail sales and pharmacy sale records. The examiner is interpreting this to be “preliminary” as it used in training the model). “Wherein the one or more machine learning models comprise an ensemble learning model configured to aggregate the one or more per-model preliminary event data objects to generate a unified predictive output” (Pg.5, particularly paragraph 0061; EN: This denotes using previous data from the network (i.e. preliminary … domain event data) to improve the training and output of the model. Pg.5, particularly paragraph 0066; EN: this denotes looking at exogenous variables such as retail sales and pharmacy sale records. The examiner is interpreting this to be “preliminary” as it used in training the model. As the claim states the ensemble can be a single model, the Examiner is interpreting the single model of the Herz reference to be the “ensemble.”).
However, Herz fails to explicitly disclose, “and the group of decomposed timeseries distributions”
Feng discloses, “and the group of decomposed timeseries distributions” (Pg.662-663, particularly section B EN: This denotes the empirical mode decomposition data being used for epidemic prediction).
Herz and Feng are analogous art because both involve epidemic prediction.
Before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Feng in order to use time-series distributions for epidemic prediction.
The motivation for doing so would be to allow the system to use “autocorrelation functions (ACF) [to be] utilized to obtain the model order of the weekly consultation rates. Based on the ACF results in Fig. 8, the value of AR order l is chosen….” (Feng, Pg.666, C1, section D) or in the case of Herz, allow the system to use the autoregressive values of the time series data to help calculate spreading predictions of the epidemic as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Feng in order to use time-series distributions for epidemic prediction.
Claim Rejections - 35 USC § 103
Claims 31 is rejected under 35 U.S.C. 103 as being unpatentable over Herz et al (US 20140095417 A1) in view of Lombardo et al (US 20030009239 A1) and Kirby et al (“Advances in spatial epidemiology and geographic information systems”) and further in view of Jafarzadeh et al (“Prediction of province-level outbreaks of foot-and-mouth disease in Iran using a zero-inflated negative binomial model”).
As per claim 31, Herz discloses, “wherein generating the … domain event prediction model comprises: (Pg.7, particularly paragraph 0084; EN: this denotes the system predicting the spread of an epidemic).
“generating, by the one or more processors, the … domain event prediction model… “
Lombardo discloses, “Inferior” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
However, Herz fails to explicitly disclose, “generating, by the one or more processors, a zero-inflated Poisson model data object for the observed … domain input data”
Jafarzadeh discloses, “generating, by the one or more processors, a zero-inflated Poisson model data object for the observed … domain input data” (pg.102, particularly section 2.2; EN: this denotes using the zero-inflated models to deal with the input data).
Herz and Jafarzadeh are analogous art because both involve epidemic prediction.
Before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Jafarzadeh in order to use zero-inflated Poisson models for input data.
The motivation for doing so would be to allow the system to because “A regular count model cannot simultaneously account for excessive non-occurrence of outbreaks (i.e. excess zeros and over dispersion, i.e. excess variation with respect to regular Poisson due to large counts. Empirically he zero-inflated model proposed in this study provided a better fit to the data” (Jafarzadeh, Pg.106, C1, second paragraph) or in the case of Herz, allow the use of zero-inflated Poisson to deal with input data that might have excess zeros as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Jafarzadeh in order to use zero-inflated Poisson models for input data.
Claim Rejections - 35 USC § 103
Claim 32 is rejected under 35 U.S.C. 103 as being unpatentable over Herz et al (US 20140095417 A1) in view of Lombardo et al (US 20030009239 A1) and Kirby et al (“Advances in spatial epidemiology and geographic information systems”) and further in view of Li et al (“Fitting mechanistic epidemic models to data: A comparison of simple Markov Chain Monte Carlo approaches”).
As per claim 32, Herz discloses, “wherein generating the simulated … domain input data comprises” (Pg.5-6, particularly paragraph 0071; EN: This denotes putting the data into the model, here the data is “simulated” because it is being placed as variables to the model, and are part of a computer system).
“determining, by the one or more processors, … the observed … domain input data” (Pg.4, particularly paragraph 0058; EN: this denotes using data associated with the geographic areas).
“Generating, by the one or more processors, the simulated … domain input data…” (Pg.5-6, particularly paragraph 0071; EN: This denotes putting the data into the model, here the data is “simulated” because it is being placed as variables to the model, and are part of a computer system).
Lombardo discloses, “Inferior” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
However, Herz fails to explicitly disclose, “and using a Gibbs-sampling based Markov chain monte carlo routine, an … domain related probability distribution of the simulated domain” and “based at least in part on the … domain related probability distribution.”
Li discloses, “and using a Gibbs-sampling based Markov chain monte carlo routine, an … domain related probability distribution of the simulated domain” and “based at least in part on the … domain related probability distribution” (Pg.1959, particularly section 2.3; EN: this denotes using Gibbs sampling Markov chain monte carlo to process data related to epidemics).
Herz and Li are analogous art because both involve epidemic prediction.
Before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Li in order to use Gibbs-sampling-based Markov Chain Monte Carlo routines for epidemic prediction.
The motivation for doing so would be to allow the system to because In the past few decades, researches have begun to adopt Bayesian approaches to disease modeling problems, Bayesian Markov Chain Monte Carlo (MCMC) is a powerful, widely used sampling based estimation approach” (Li, pg.1957, Introduction, second paragraph) or in the case of Herz, allow the system to use well known algorithms for disease modeling as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Li in order to use Gibbs-sampling-based Markov Chain Monte Carlo routines for epidemic prediction.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 21-24, 29-30, 33, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Herz et al (US 20140095417 A1) in view of Lombardo et al (US 20030009239 A1).
As per claims 39-40, Herz discloses, “A system comprising: one or more processors; and one or more memories storing processor-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising” (abstract; EN; this denotes a computer system which inherently includes memory, processors, computer code, and the like in order to execute the actions of the system).
“generating forecasted … domain event data” (Pg.7, particularly paragraph 0084; EN: this denotes the system predicting the spread of an epidemic). “… for a geographic domain” (Pg.7, particularly paragraph 0085; EN: this denotes the consideration of geographic locations for spreading disease). “at a forecasting period” (Pg.7, particularly paragraph 0083; EN: this denotes the forecast of the spread over time). “based at least in part on …. Observed data for the … geographic domain” (Pg.4, particularly paragraph 0058; EN: this denotes using data associated with the geographic areas). “at an observation period” (pg.4, particularly paragraph 0058; EN: this denotes using the data collected at set intervals).
“generating an … domain event prediction model … based at least in part on… observed data for the … geographic domain at the observation period” (pg.4, particularly paragraph 0059; EN: this denotes using a Bayesian belief network to make predictions).
“generating simulated … domain input data for the … geographic domain at the forecasting period” (Pg.5-6, particularly paragraph 0071; EN: This denotes putting the data into the model, here the data is “simulated” because it is being placed as variables to the model, and are part of a computer system).
“generating forecasted … domain even data for the …. Geographic domain at the forecasting period by processing the simulated … domain input data in accordance with the … domain event prediction model” (pg.5-6, particularly paragraph 0071; EN: this denotes the system predicting potential epidemics based upon the data).
“initiating one or more prediction based actions based at least in part on the forecasted … domain event data” (pg.5-6, particularly paragraph 0071; EN: this denotes the system predicting potential epidemics based upon the data and alerting authorities about the epidemic).
However, Herz fails to explicitly disclose “superior domain” and “for a hierarchically superior geographic domain”, “ Superior”, “hierarchically superior”, “inferior domain”, “a hierarchically inferior geographic domain of the hierarchically superior geographic domain”, “inferior”, “hierarchically inferior geographic domain.”
Lombardo discloses, “superior domain”, “for a hierarchically superior geographic domain”, “ Superior”, “hierarchically superior”, “inferior domain”, “a hierarchically inferior geographic domain of the hierarchically superior geographic domain”, “inferior”, “hierarchically inferior geographic domain” (pg.4, particularly paragraph 0060; EN: this denotes collecting data and implementing predictions at multiple resolutions of data, with the resolutions being hierarchical such that national > state > county > district > neighborhood. Pg.3, particularly paragraph 0042; EN: this denotes the lowest hierarchical level of the data being the zip code level).
Herz and Lombardo are analogous art because both involve epidemic prediction.
Before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Lombardo in order to allow predictions to be made at various hierarchical levels.
The motivation for doing so would be to allow the system to “be applied simultaneously over multiple time scales. In particular, depending on the resolution of the data, they can be applied at the neighborhood, district, county, state, or national levels” (Lombardo, Pg.4, paragraph 0060) or in the case of Herz, allow the system to be applied at different geographic scales as needed by the system to predict epidemics in monitored regions.
Therefore before the effective filing date it would have been obvious to one skilled in the art of epidemic prediction to combine the work of Herz and Lombardo in order to allow predictions to be made at various hierarchical levels.
Response to Arguments
In pg.10, the Applicant argues in regards to the rejection under U.S.C. 101,
Second, the Office Action uses overbroad generalizations to allege that the forecasting techniques of the claims are directed to mental processes. See e.g., Office Action, p. 9. This type of analysis was rejected in Ex Parte Desjardines, where the Appeals Review Panel noted that "examiners and panels should not evaluate claims at such a high level of generality." Ex Parte Desjardines, p. 9. In Ex Parte Desjardines, the Panel acknowledged that "categorically excluding AI innovations from patent protection in the United States jeopardizes America's leadership in this critical emerging technology." Id. The same applies to the forecasting techniques recited by the claims. Here, the Specification, as filed, is clear that the "present invention introduces data forecasting techniques that outperform state-of-the-art data forecasting technique, especially with regard to long-term trend forecasting." Specification " [0023]. Thus, just like the claims in Ex Parte Desjardines, the present claims recite an improved technique that enhances the performance of a computer (i.e., with respect to forecasting). Compare Specification 1 [0023] to Ex Parte Desjardines, p. 9. For example, in Ex Parte Desjardines, the claimed training technique improved the storage capacity of a computer. Here, the claimed forecasting technique improves the efficiency and reliability of various existing predictive data analysis frameworks, including various existing distributed predictive data analysis frameworks. See Specification " [0022]- [0023]. Thus, like the claims in Ex Parte Desjardines, the present claims are directed to patent eligible subject matter under 35 U.S.C. § 101.
In response, the Examiner maintains the rejection as shown above. Applicant appears to be arguing that by improving the “forecasting” they have somehow improved the computer. However, the forecasting aspect of this claim is the abstract idea. This is not an improvement to the computer in any way. Putting less data into a computer and therefore using less memory does not improve the computer. “Forecasting” is not a technology that can be improved, it is an abstract idea, and therefore the rejection is maintained as shown above.
In pg.12, the Applicant argues in regards to the rejection of the independent claims under U.S.C. 101,
(emphasis added). As amended, claim 21 recites a forecasting process in which simulated data is generated to forecast, validate, and then act on information at a future point in time. Each of the operations includes simulated data, which cannot be performed within the human mind. For example, the human mind cannot practically (i) generate forecasted superior event data, (ii) generate a model based on the forecasted superior event data, (iii) generate simulated inferior domain input data, (iv) generate forecasted inferior domain event data, (v) generate confirmed inferior domain event data, or (vi) initiate prediction-based actions. Accordingly, no element of claim 21, as amended, under its broadest reasonable interpretation may be considered a mental process as defined by the MPEP. For at least this reason, Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101 because the claimed invention is not directed to a judicial exception under prong one of Step 2A.
In response the Examiner maintains the rejection as shown above. Applicant appears to be arguing that using “simulated data” somehow cannot be performed in the human mind or by pencil and paper. All math performed by hand is “simulated data.” If one writes a word problem where 3 apples are added to 2 apples, to determine that you now have five apples, the apples are not actually assembled into groups then added together to show the final result. The numbers “simulate” the real life apples in order to solve the problem without requiring the apples to actually be there. Merely “simulating data” does not cause the claim to be significantly more than the abstract idea, and therefore the rejection is maintained as shown above.
In pg.12-14 Applicant makes similar arguments to the improvement of the technology of “forecasting.” As stated above, “forecasting” is not a technology, nor is predictive data analysis. Making prediction is something that can be performed mentally or with pencil and paper. Merely using generic machine learning algorithms and computer equipment does not cause this to be an improvement to a technology. Using less data or performing more accurate predictions are not an improvement to the technology, it is an improvement to the abstract idea. Therefore the rejection is maintained as shown above.
In pg.15, the Applicant states that claims 39-40 have been amended to include similar amendments to claim 21. However, this is untrue. Claims 39-40 were not amended similarly to claim 21, and do not include the limitations argued here. Therefore their rejections are maintained as shown in the previous office action.
Applicant's arguments with respect to claims 21-32 and 34-41 have been considered but are either moot in view of the new ground(s) of rejection or repetitions of the above arguments.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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/BEN M RIFKIN/Primary Examiner, Art Unit 2123