DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Applicant's response filed 9/1/2025 has been fully considered. The following rejections
and/or objections are either reiterated or newly applied.
Status of Claims
Claims 1 and 3-4 are pending and examined on the merits.
Claims 2 and 5 are canceled.
Priority
The instant application is a continuation of application of PCT/CN2021/117650 having and international filing date of Sept 10, 2021 which further claims the benefit of foreign priority to patent application No. 202110969578.8 filed on Aug 23, 2021. Thus, the effective filing date of the claims is Aug 23, 2021.
Withdrawn Rejections
35 USC § 112(b)
The rejection of claim 3 under 35 U.S.C. 112(b) withdrawn in view of Applicant's claim amendments.
The rejection of claim 5 under 35 U.S.C. 112(b) withdrawn in view of Applicant's claim cancelation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 3-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental process, a mathematical concept, organizing human activity, or a law of nature or natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). In the instant application, the claims recite the following limitations that equate to an abstract idea:
Claim 1 (currently amended): “dividing the predetermined time period into a plurality of time periods based on a time mode” provides a mathematical calculation (dividing time periods requires calculation) that is considered a mathematical concept, which is an abstract idea.
“dividing the plurality of target regions into a plurality of spatial nodes based on a spatial mode” provides a mathematical calculation (dividing target regions requires calculation) that is considered a mathematical concept, which is an abstract idea.
“constructing a simulation model according to the population movement flow, the plurality of time periods, and the plurality of spatial nodes” provides a mathematical relationship (building a simulation or model captures mathematical relationships) that is considered a mathematical concept, which is an abstract idea.
“analyzing, comparing, and summarizing characteristics of a spread of the epidemic under different time models and different spatial models, and thereby realizing a prediction and simulation of infectious diseases” provides an evaluation and comparison (analyzing and comparing characteristics of an epidemic) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea.
“obtaining a population movement index between the plurality of target regions according to the case data, the population number and the spatial range data” provides a mathematical calculation (obtaining population movement indices from the previously obtained case data requires calculation) that is considered a mathematical concept, which is an abstract idea.
“performing a data characterization on the population movement index to obtain the population movement flow between the plurality of target regions” provides a mathematical relationship (building a simulation or model captures mathematical relationships) that is considered a mathematical concept, which is an abstract idea.
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“the simulation model is represented as:” provides mathematical equations (the simulation model represented as equations) that is considered a mathematical concept, which is an abstract idea.
Claim 2: canceled
Claim 3 (currently amended): “dividing each of the target regions according to a spatial scale of n meters x n meters and a time scale of s hours to obtain a plurality of intervals corresponding to all the target regions” provides a mathematical calculation (dividing target regions requires calculation) that is considered a mathematical concept, which is an abstract idea.
“integrating a signaling data of the mobile phones user’s signal data at a current time scale in each of the intervals, obtaining a movement flow of the users at the current time scale in each of the intervals, and realizing the data characterization on the population movement index” provides a mathematical relationship (determining population flow between regions) that is considered a mathematical concept, which is an abstract idea.
Claim 4: “equidistantly dividing the predetermined time period” provides a mathematical calculation (dividing target time periods requires calculation) that is considered a mathematical concept, which is an abstract idea.
“dividing the predetermined time period according to a work and rest regular pattern of the population” provides a mathematical calculation (dividing time periods requires calculation) that is considered a mathematical concept, which is an abstract idea.
Claim 5: canceled
These recitations are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)) and comparing information regarding a sample or test to a control or target data in Univ. of Utah Research Found. v. Ambry Genetics Corp. (774 F.3d 755, 113 U.S.P.Q.2d 1241 (Fed. Cir. 2014)) and Association for Molecular Pathology v. USPTO (689 F.3d 1303, 103 U.S.P.Q.2d 1681 (Fed. Cir. 2012)) that the courts have identified as concepts that can be practically performed in the human mind or are mathematical relationships. Therefore, these limitations fall under the “Mental process” and “Mathematical concepts” groupings of abstract ideas. As such, claims 1 and 3-4 recite an abstract idea (Step 2A, Prong 1: YES).
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to
determine if the claims as a whole integrate the recited judicial exception into a practical application or
not (Step 2A, Prong 2). The judicial exceptions listed above are not integrated into a practical application
because the claims do not recite an additional element or elements that reflects an improvement to
technology. Specifically, the claims recite the following additional elements:
Claim 1 (currently amended): “obtaining a population movement flow between a plurality of target regions within a predetermined time period” provides insignificant extra-solution activities (obtaining data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
“obtaining a case data, a population number and a spatial range data in the plurality of target regions” provides insignificant extra-solution activities (obtaining case data is a pre-solution activity involving data gathering steps) that do not serve to integrate the judicial exceptions into a practical application.
Claim 2: canceled
The steps for obtaining data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre-solution activities involving data gathering steps (see MPEP 2106.04(d)(2)).
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application, or equate to mere instructions to apply the recited exception in a generic way or in a generic computing environment.
The limitations for obtaining data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application. Furthermore, no inventive concept is claimed by these limitations as they are demonstrated by Jain et al. (US-11056242) to be well-understood, routine, and conventional: Page 31 col 2 lines 1-4 "The system can obtain monitoring data from individuals on an ongoing or continual basis using digital platforms, as well as obtain aggregated data regarding communities and the effects of a disease." Cao et al. (CN-112885483) reinforces the assertion that no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional: Page 3 second paragraph "Aiming at the epidemic situation, a large number of scholars collect the epidemic situation data and develop a series of researches such as the prediction of the epidemic situation and the evaluation of epidemic prevention measures." Finally, Bao et al. (CN-113241188) also reinforces the assertion that no inventive concept is claimed by these limitations as they are well-understood, routine, and conventional: Page 5 first paragraph "In today with extremely developed traffic networks, viruses can rapidly spread to multiple areas in local outbreaks through traffic connections, and accurate simulation of infectious disease development situation must be based on big data of travel."
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1 and 3-4 are not patent eligible.
Response to Arguments under 35 USC § 101
Applicant’s arguments filed 9/1/2025 are fully considered but they are not persuasive.
Regarding claim 1, Applicant asserts that the amendments of “analyzing, comparing, and summarizing characteristics of a spread of the epidemic under different time models and different spatial models, and thereby realizing a prediction and simulation of infectious diseases” renders the claim as not directed to an abstract idea (Remarks 9/1/2025 Pages 1-2).
The Examiner has indicated above that these additional features provide an evaluation and comparison (analyzing and comparing characteristics of an epidemic) that may be performed in the human mind and is therefore considered a mental process, which is an abstract idea. Furthermore, the Examiner has also indicated that the additional elements of obtaining data are insignificant extra-solution activities that do not serve to integrate the recited judicial exceptions into a practical application because they are pre-solution activities involving data gathering steps (see MPEP 2106.04(d)(2)). Finally, the Examiner has indicated that no inventive concept is claimed by these limitations and are well-understood, routine, and conventional as evidenced by the quoted references above.
Therefore, the rejection of claim 1 is maintained. Claims 3-4 depend from independent claim 1 and are likewise rejected for these reasons.
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 1 and 4 rejected under 35 U.S.C. 103 as being unpatentable over Bao et al. (CN-113241188) in view of Cao et al. (CN-112885483).
Regarding claim 1, Bao teaches an infectious disease simulation model (Page 1 Abstract "a regionally differentiated infectious disease dynamics prediction model is established.").
Bao provides obtaining a population movement flow between a plurality of target regions within a predetermined time period (Page 3 second paragraph "rhoj Calculating the population proportion of each region flowing out of the epidemic situation center as a region share parameter").
Bao also provides constructing a simulation model according to the population movement flow, the plurality of time periods, and the plurality of spatial nodes (Page 2 second to last sentence "establishing multi-region time-varying SEIR model based on population mobility network").
Bao teaches obtaining a case data including population and geography information (Page 1 Abstract "the model parameters are estimated based on the actual data to simulate the population dynamics in each warehouse in multiple regions").
Bao does not explicitly teach dividing the predetermined time period into a plurality of time periods based on a time mode (interpreted here as how time periods are divided) nor dividing the plurality of target regions into a plurality of spatial nodes based on a spatial mode (interpreted here as how space is divided), nor obtaining a population movement index between the plurality of target regions according to the case data, the population number and the spatial range data, nor performing a data characterization on the population movement index to obtain the population movement flow between the plurality of target regions.
However, Cao teaches dividing the duration of an epidemic period into five stages (Page 12 paragraph 2 "the epidemic situation period from 1 day of month to 6 days of year is divided into five time stages according to the government epidemic prevention measures and medical conditions, and a training set and a test set are divided for each time stage").
Cao also implies dividing geography for analysis of space-time migration (Page 5 paragraph 5 "the embodiment of the invention creates a geography model of the population, analyzes the space-time migration characteristics of urban residents and the health state transformation characteristics of infected persons, constructs a small-world network, and establishes a COVID-19 infectious disease model.").
Cao also teaches obtaining real population statistics and travel data that together can be called a population movement index, along with characterizing the data into a set of rules (Page 2 claim 2 "According to the real population statistics data and real population travel data of the real city, the migration rules of each agent are determined.").
Finally, the formulas in claim 1 representing the simulation model may be implemented by a person skilled in the art in combination with an existing SEIR model (or other variation of this model type) by means of a limited amount of reasoning and experiments by adding the factors of time and space to the models taught by either Bao or Cao (Bao pages 2-3 "The multi-zone time-varying SEIR model", and Cao page 3 second paragraph "Yang et al evaluated government control measures and made authoritative prediction of epidemic development trends by modifying the SEIR in conjunction with the trained Artificial Intelligence (AI) approach. The [model] combines an SIR model with a machine learning method, evaluates the severity of the epidemic situation of the important provinces and cities in China, and predicts the final number of people to be diagnosed").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Bao as taught by Cao in order to generate training and test data sets for the infectious disease model (Page 12 paragraph 2 "the cumulative number of confirmed persons reported each day is used as a known data set, and the data set is divided into two mutually exclusive sets, namely a training set and a testing set, by a leave-out method"). One skilled in the art would have a reasonable expectation of success because both approaches are modeling infectious disease through time and space.
Regarding claim 4, Cao teaches dividing the predetermined time period according to a work and rest regular pattern of the population (Page 7 paragraph 4 "In some examples, the crowd types include students, office workers, and retirees. The travel data of the real population of the three types of people in the real city can be analyzed respectively, and the travel modes of the real population of the three types of people can be determined. Specifically, the travel route of the student can be summarized as: 0-7 at home, 7-17 at school, and 17-0 at home." and further spatiotemporal breakdowns for each "crowd type".").
Claim 3 rejected under 35 U.S.C. 103 as being unpatentable over Bao et al. (CN-113241188) in view of Cao et al. (CN-112885483) as applied to claims 1 and 4, and further in view of Jain et al. (US-11056242).
Bao et al. in view of Cao et al. are applied to claims 1 and 4.
Regarding claim 3, Bao teaches integrating mobile phone users’ signal data at a current time scale in each of the intervals and obtaining a movement flow of the users at the current time scale in each of the intervals in order to characterize population movement (Page 3 third paragraph "the inflow and outflow superposition exogenous treatment of the infected people and the susceptible people in each area is carried out between the areas through the big population mobility data").
Bao does not explicitly teach dividing each of the target regions according to a spatial scale of n meters x n meters and a time scale of s hours to obtain a plurality of intervals corresponding to all the target regions.
However, Jain teaches a fine-grained determination of population level at specific regions or subsections of a community (Page 69 col 2 lines 61-67 "The population data can indicate the population level at different locations. This can include measures of population, population density, occupancy, traffic, etc. Population can be determined at a fine-grained level, e.g., for specific regions or portions of a community, such as for different neighborhoods, city blocks, subdivisions, buildings or even for specific addresses.").
Therefore, it would have been obvious to one of ordinary skill in the art as of the effective filing date of the claimed invention to modify the methods of Bao and Cao as taught by Jain in order to obtain fine-grained data in both time and space (Page 69 col 2 lines 55-60 "The passive sensing data includes the recorded data streams from the various users. The sensor data for each user can be associated with metadata indicating the context in which the data capture occurred (e.g., a timestamp, a location, etc.), as well as a user identifier for the associated user and a community identifier for the user's community."). One skilled in the art would have a reasonable expectation of success because both approaches are using the same data type for the purposes of predicting population at specific points in time and space.
Response to Arguments under 35 USC § 103
Applicant’s arguments filed 9/1/2025 are fully considered but they are not persuasive.
Regarding Claims 1 and 3-4, Applicant argues that, with respect to amended claim 1 which now incorporates the limitations of currently canceled claims 2 and 5, Cao does not teach the details of the "data characterization", nor "any possibility of vectors" as recited in amended claim 1 (Remarks 9/1/2025 Pages 4-5). However, the Examiner notes that the relied upon excerpt from Cao, a determination of migration rules based on real population statistics and travel data, does encompass the performance of describing data of population movement flow between regions. Additionally, Examiner notes that Cao does in fact teach utilizing data vectors (page 11, paragraph 4 "The original data of the embodiment of the invention are divided into three types, namely basic geographic information vector data (including building outline vector data and land utilization type vector data), street population census data, real-time epidemic situation data based on a clove garden and daily travel intensity of big data statistics of Unicom mobile phone signaling").
Applicant additionally submits that Cao is silent on specific applications and computation aspects of the algorithmic model, specifically the susceptible, exposed, pre-symptomatic, and infected parameters of the model (Remarks 9/1/2025 Page 6). The Examiner notes that Applicant concedes that Cao relates to a SEIR model, which inherently utilizes the variables of "susceptible persons" (S), "exposed persons" also designated as latent infected or pre-symptomatic persons (E), "infected persons" (I), and “recovered persons” (R). As such, the parameters of susceptible, exposed, pre-symptomatic, and infected are indeed applied in the computational aspects of the algorithmic model of Cao.
Applicant also asserts that "a computational implementation process based on algorithm models is totally different from the limitations of Cao" (Remarks 9/1/2025 Page 6), but fails to expound upon any specific feature in which they differ. The Examiner reiterates the 103 rejection of the formula limitations of amended claim 1: the formulas in claim 1 representing the simulation model may be implemented by a person skilled in the art in combination with an existing SEIR model (or other variation of this model type) by means of a limited amount of reasoning and experiments by adding the factors of time and space to the models taught by either Bao or Cao (Bao pages 2-3 "The multi-zone time-varying SEIR model", and Cao page 3 second paragraph "Yang et al evaluated government control measures and made authoritative prediction of epidemic development trends by modifying the SEIR in conjunction with the trained Artificial Intelligence (AI) approach. The [model] combines an SIR model with a machine learning method, evaluates the severity of the epidemic situation of the important provinces and cities in China, and predicts the final number of people to be diagnosed").
Therefore, the rejection of claims 1 and 3-4 are maintained.
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.
Claim 1 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of US-20220138373 in view of Cao et al. (CN-112885483) and Bao et al. (CN-113241188). Although the claims at issue are not identical, they are not patentably distinct from each other because both involve modeling population count and movement at specific points in space and time, as well as obtaining the necessary data for testing. They both also have the classic SEIR variables of susceptible, exposed, infected, and recovered numbers of individuals in the population being modeled. Finally, they both also capture rates of removal, morbidity, and infection in each respective model or simulation.
While US-20220138373 does not explicitly teach dividing time periods or target regions based on different modes, incorporating population movement data into the model, nor obtaining real population statistics and travel data that together can be called a population movement index, along with characterizing the data into a set of rules, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Cao as described above for claim 1 of the instant application, in order to generate training and test data sets for the infectious disease model (Page 12 paragraph 2 "the cumulative number of confirmed persons reported each day is used as a known data set, and the data set is divided into two mutually exclusive sets, namely a training set and a testing set, by a leave-out method"). One skilled in the art would have a reasonable expectation of success because both approaches are modeling infectious disease through time and space.
While US-20220138373 nor Cao explicitly teach obtaining case data including population and geography information, it would have been obvious to one of ordinary skill in the art to modify these methods, with those taught by Bao as described above for claim 1 of the instant application, in order to reflect the size of population flowing into or out of an area (Page 6 second to last paragraph “Population mobility network data is estimated according to a 'Baidu mobility' big data platform, and the database can reflect the size of population flowing into or out of an area based on the change of the geographic position of the mobile device of the user"). One skilled in the art would have a reasonable expectation of success because both approaches are modeling infectious disease through time and space.
Response to Arguments under Double Patenting
Applicant’s arguments filed 9/1/2025 are fully considered but they are not persuasive.
Regarding Claim 1, Applicant argues that the amended features contained in the specification are “different from US-20220138373 in view of Cao”, as discussed above for 35 USC § 103 (Remarks 9/1/2025 Page 7). The Examiner notes, for the reasons above, that Cao (and now Bao, as necessitated by Applicant’s amendments) does indeed encompass the limitations of amended claim 1. Therefore, the rejection of claim 1, on the ground of nonstatutory double patenting as being unpatentable over claim 1 of US-20220138373 in view of Cao (and Bao), is maintained. Therefore the rejection of claims 3-4 is also maintained, as they are dependent on claim 1.
Conclusion
No claims are allowed.
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 TH REE-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 finaI action.
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/R.A.P./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686