CTNF 18/400,160 CTNF 93287 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of Claims This Office action is in response to correspondence received December 29, 2023. Claims 1-8 are pending and have been examined. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s): Claims 1, 7, and 8, which are similar in scope: calculate an actual measurement value representing a growth situation of a crop in a farm field; acquire weather information in the farm field in which the crop is planted; calculate a trial calculation growth curve representing a transition of a growth situation in calculation by making a trial calculation of a transition of a growth situation of the crop toward a target yield using environment information including weather information after planting the crop in the farm field and information about the target yield of the crop; and [presenting] both the actual measurement value of the growth situation of the crop and the trial calculation growth curve. This describes a mental process which is an abstract idea because the steps are of observation and judgment. Calculating a measurement is a mental judgment; acquiring weather information is an observation; calculating a growth curve is a mental judgment; and presenting the measurement can be done on pen and paper. Alternatively this describes a mathematical relationship of calculating and presenting the results of the calculation. Therefore claims 1, 7, and 8 recite a mental process. This judicial exception is not integrated into a practical application because the elements alone and in combination amount to instructions to apply the above identified abstract idea to a generic computer. See MPEP 2106.05(f)(2). The elements are: Claim 1: apparatus a memory configured to store instructions; and a processor configured to execute the instructions to: perform display control of displaying Claim 7: method executed by a computer, Claim 8: A non-transitory computer readable medium that stores a computer program for causing a computer to execute the steps of. Therefore, there is not a practical application of an abstract idea. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the reasoning in the practical application section is carried over: for the same reasons that applying a generic computer to an abstract idea is not a practical application, it is not significantly more than an abstract idea. Per the dependent claims: Claims 2-6 further describe the abstract idea of claim 1. Therefore claims 1-8 are rejected under 35 USC 101. Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15-aia AIA Claim(s) 1-8 are is/are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Shriver et al., US PGPUB 20160224703 A1 (“Shriver”) . Per claims 1, 7, and 8, which are similar in scope, Shriver teaches A growth information providing apparatus comprising: a memory configured to store instructions; and a processor configured to execute the instructions to: in par 026: “Each module of the plurality can be entirely or partially executed, run, hosted, or otherwise performed by: a remote computing system (e.g., a server), a user device (e.g., a native application, web application, or firmware on the user device), or by any other suitable computing system. When one or more modules are performed by the remote computing system, the remote computing system can remotely (e.g., wirelessly) communicate with or otherwise control user device operation.” Then Shriver teaches calculate an actual measurement value representing a growth situation of a crop in a farm field ; in par 026: “The data used by the modules preferably includes the set of remote measurement values recorded for the geographic region (e.g., measurements of the crops within the field being analyzed),” Then Shriver teaches acquire weather information in the farm field in which the crop is planted ; in par 039: “The underlying data preferably includes an image, more preferably a remote image, but can alternatively include a plurality of images, a subset of an image, weather measurements, ” Then Shriver teaches calculate a trial calculation growth curve representing a transition of a growth situation in calculation by making a trial calculation of a transition of a growth situation of the crop toward a target yield using environment information including weather information after planting the crop in the farm field and information about the target yield of the crop in par 027: “All or a subset of the modules can be run or updated: once; every year (e.g., after crop harvest, after crop yield is determined, etc.); every time the method is performed; e very time an unanticipated measurement value is received (e.g., every time the measured vegetation index value deviates beyond a threshold deviation from the anticipated vegetation index value estimated by the growth curve); or at any other suitable frequency. The modules can be run or updated concurrently, serially, at varying frequencies, or at any other suitable time .” See also par 057: “The transition date can subsequently be used (e.g., by the user or automatically) to plan future treatments, schedule future operations, forecast harvest parameters (e.g., yield), or used in any other suitable manner. The transition date can be later verified using subsequently collected data (e.g., data collected after the transition date for the field, full growing season data for the field). The transition date determination model can additionally be modified based on the discrepancy between the actual transition date and the estimated transition dates.” See also par 058: “For example, the growth curve shape can be flattened or the transition date delayed when the geographic region has suffered from a drought or heat stress during the growing period to date. Alternatively, the growth curve shape can be compressed or the transition date accelerated if growing conditions were favorable during the growing period to date.” Then Shriver teaches and perform display control of displaying both the actual measurement value of the growth situation of the crop and the trial calculation growth curve in par 068: “When the selected reference curve is validated, the transition date for the geographic region is preferably maintained (e.g., kept the same) or re-determined from the selected reference cur ve. When the selected reference curve is invalidated, the method can additionally include selecting a new reference curve based on the updated measurement value set (e.g., including the new measurement value), example shown in FIG. 5. The new reference curve can be selected or classified by the growth curve selection module previously used to select the invalidated reference curve, by a retrained growth curve selection module, by a secondary growth curve selection module, or by any other suitable growth curve selection module.” See also Fig 6, reference curve 1 and 2, see also par 064: “The measurement value set can be classified (e.g., as shown in FIG. 6), categorized, or otherwise labeled based on the measurement values within the set, similarities between the measurement value set and historic or model measurement value sets (e.g., similar parameters, factors, etc.), geographic region parameters (e.g., environmental parameter values), growing season parameters, or based on any other suitable parameter, in combination or alone. Selecting the reference curve can additionally include extracting values of key features (feature values) from the measurement value set or otherwise processing the measurement value set prior to reference curve selection, wherein the extracted values can be used in reference curve selection. In a specific example, the reference curve is selected using a random forest model.” See also par 092: “The method can additionally include providing the estimated growth stage to a user, which functions to inform the user of the estimated growth stage that their crops or fields are in. Providing the estimated growth stage to a user can include : storing the estimated growth stage and/or growth phase in association with the user account; transmitting (e.g., wirelessly or through a wire) and/or displaying the estimated growth stage and/or growth phase to a user device associated with the user accoun t;” Per claim 2, Shriver teaches the limitations of claim 1, above. Shriver further teaches calculate, as an actual measurement value representing a growth situation of a crop, a plurality of kinds of actual measurement values including an actual measurement value representing a growth situation of a leaf of the crop in par 028: “he data can be remote data, such as remotely-sensed images (e.g., satellite images or drone-captured images), but can alternatively or additionally determine the measurement values from in-field measurements or from any other suitable data source. The measurement module preferably receives remote data from the remote data source and extracts the measurement values in response to remote data receipt, but can alternatively determine the measurement values in any other suitable manner. The measurement values can be automatically determined from the remote data (e.g., in response to remote data receipt), automatically determined in response to receipt of a user request for the growth stage, received from a user device or user account, or be otherwise determined.” See also par 036: “. The growth stage module preferably receives the determined growth phase from the growth phase module, determines (e.g., retrieves, selects, calculates, estimates, etc.) a growth phase model based on the determined growth phase; receives the number of aggregate growing degree days from the aggregate growing degree day module; a nd determines the instantaneous growth stage of the crop based on the determined growth phase model and number of aggregate growing degree day s. The growth phase model can be a vegetative model, reproductive model, or any other suitable growth phase model. The growth phase model can be determined based on historic data, received from a third party, or otherwise determined. The growth phase mode l can be specific to the cultivar, specific to the geographic region, specific to the treatment, specific to the user account, global, or be any other suitable growth phase model. The growth phase model can be updated (e.g., at a predetermined frequency, in response to a discrepancy between the actual and anticipated measurement values, in response to receipt of a user request, etc.) or remain constant.” See also par 039: “The underlying data from which the measurement value is determined is preferably remote data of the field (e.g., data gathered by a sensor remote from the field, such as remote images), but can alternatively be proximal data (e.g., data gathered by a sensor proximal the plant or within the field) or be any other suitable type of data. The underlying data is preferably recorded for the field, but can alternatively be recorded at a second geographic region associated with the field. For example, the underlying data is preferably of the field being analyzed, but can alternatively be recorded at a secondary field proximal the field being analyzed. The underlying data preferably includes an image, more preferably a remote image, but can alternatively include a plurality of images, a subset of an image, weather measurements, soil measurements, crop type assignments, or any other suitable set of data. Remote images can include satellite images, drone images, measurements recorded by a terrestrial system (e.g., soil measurements, water measurements, stalk force measurements, etc.), or be any other suitable data about the crops within the field.” See also par 042: “The image is preferably associated with one or more temporal indicators (e.g., time durations, time units). The temporal indicator can be a time point relative to a time duration, an absolute time (e.g., indicated by a global timestamp), or any other suitable measure of time. The time duration can be a unique or non-unique time duration. Examples of time durations include a unique year (e.g., 2015), a unique growing season (e.g., spring of 2014, fall of 2009, etc.), a growth stage (e.g., vegetative stage, reproductive stage), relative time duration (e.g., spring, growth duration), or any other suitable time duration.” Per claim 3, Shriver teaches the limitations of claim 2, above. Shriver further teaches detect a transition of a growth stage of a crop using a change tendency of an actual measurement value based on a transition of the actual measurement value representing a growth situation of a leaf of the crop in par 035: “The growth phase module can determine that the crops are in a vegetative phase in response to the given date preceding the transition date, and determine that the crops are in a reproductive phase in response to the given date exceeding the transition date .” See also par 042: “The time duration can be a unique or non-unique time duration. Examples of time durations include a unique year (e.g., 2015), a unique growing season (e.g., spring of 2014, fall of 2009, etc.), a growth stage (e.g., vegetative stage, reproductive stage), relative time duration (e.g., spring, growth duration), or any other suitable time duration.” Per claim 4, Shriver teaches the limitations of claim 1, above. Shriver further teaches determine whether growth of a crop is delayed by comparing an actual measurement value with a trial calculation growth curve, in par 058: “For example, the growth curve shape can be flattened or the transition date delayed when the geographic region has suffered from a drought or heat stress during the growing period to date . Alternatively, the growth curve shape can be compressed or the transition date accelerated if growing conditions were favorable during the growing period to date.” Shriver then teaches and wherein in a case where the growth of the crop is delayed, the processor is configured to execute the instructions to: perform display control of displaying handling reference information serving as a reference to handle the delay in par 092: “The method can additionally include providing the estimated growth stage to a user, which functions to inform the user of the estimated growth stage that their crops or fields are in. Providing the estimated growth stage to a user can include: storing the estimated growth stage and/or growth phase in association with the user account; transmitting (e.g., wirelessly or through a wire) and/or displaying the estimated growth stage and/or growth phase to a user device associated with the user account; generating a crop summary report for the user, generating a crop growth history for the user (e.g., wherein the user can scroll through different time points and see the respective growth stage, corresponding images, etc.), or otherwise enabling user access to the estimated growth stage and/or growth phase.” Per claim 5, Shriver teaches the limitations of claim 1, above. Shriver further teaches calculate a plurality of trial calculation growth curves related to a plurality of target yields different from each other in par 067: “In this variation, the method can additionally include validating the reference curve previously selected for a geographic region (selected reference curve), based on a new measurement value for the geographic region S240. Validating the selected reference curve (e.g., verifying the selected reference curve) S240 can include: comparing the second measurement value with the measurement value anticipated by the reference curve, validating the selected reference curve when the second measurement value substantially matches the anticipated measurement value, and invalidating the selected reference curve when the second measurement value substantially deviates from the anticipated measurement value. However, the selected reference curve can be otherwise validated or invalidated based on the new measurement value. The growth curve selection module is preferably reinforced when the selected reference curve is validated, but can remain unadjusted or otherwise adjusted in response to selected reference curve validation.” See also par 093: “Similar or common parameters can include: yield goals , geographic proximity, crop varietal, weather patterns, soil characteristics, past treatments for the instantaneous growing season, historic treatments for the geographic region (e.g., across multiple growing seasons), past treatment timing, or any other suitable parameter.” Shriver then teaches and perform display control of displaying a trial calculation growth curve selected from among the calculated trial calculation growth curves in par 068: “When the selected reference curve is validated, the transition date for the geographic region is preferably maintained (e.g., kept the same) or re-determined from the selected reference cur ve. When the selected reference curve is invalidated, the method can additionally include selecting a new reference curve based on the updated measurement value set (e.g., including the new measurement value), example shown in FIG. 5. The new reference curve can be selected or classified by the growth curve selection module previously used to select the invalidated reference curve, by a retrained growth curve selection module, by a secondary growth curve selection module, or by any other suitable growth curve selection module.” See also Fig 6, reference curve 1 and 2, see also par 064: “The measurement value set can be classified (e.g., as shown in FIG. 6), categorized, or otherwise labeled based on the measurement values within the set, similarities between the measurement value set and historic or model measurement value sets (e.g., similar parameters, factors, etc.), geographic region parameters (e.g., environmental parameter values), growing season parameters, or based on any other suitable parameter, in combination or alone. Selecting the reference curve can additionally include extracting values of key features (feature values) from the measurement value set or otherwise processing the measurement value set prior to reference curve selection, wherein the extracted values can be used in reference curve selection. In a specific example, the reference curve is selected using a random forest model.” See also par 092: “The method can additionally include providing the estimated growth stage to a user, which functions to inform the user of the estimated growth stage that their crops or fields are in. Providing the estimated growth stage to a user can include : storing the estimated growth stage and/or growth phase in association with the user account; transmitting (e.g., wirelessly or through a wire) and/or displaying the estimated growth stage and/or growth phase to a user device associated with the user accoun t.” Per claim 6, Shriver teaches the limitations of claim 1, above. Shriver further teaches perform display control of displaying environment information about a farm field in par 068: “When the selected reference curve is validated, the transition date for the geographic region is preferably maintained (e.g., kept the same) or re-determined from the selected reference cur ve. When the selected reference curve is invalidated, the method can additionally include selecting a new reference curve based on the updated measurement value set (e.g., including the new measurement value), example shown in FIG. 5. The new reference curve can be selected or classified by the growth curve selection module previously used to select the invalidated reference curve, by a retrained growth curve selection module, by a secondary growth curve selection module, or by any other suitable growth curve selection module.” See also Fig 6, reference curve 1 and 2, see also par 064: “The measurement value set can be classified (e.g., as shown in FIG. 6), categorized, or otherwise labeled based on the measurement values within the set, similarities between the measurement value set and historic or model measurement value sets (e.g., similar parameters, factors, etc.), geographic region parameters (e.g., environmental parameter values), growing season parameters, or based on any other suitable parameter, in combination or alone. Selecting the reference curve can additionally include extracting values of key features (feature values) from the measurement value set or otherwise processing the measurement value set prior to reference curve selection, wherein the extracted values can be used in reference curve selection. In a specific example, the reference curve is selected using a random forest model.” See also par 092: “The method can additionally include providing the estimated growth stage to a user, which functions to inform the user of the estimated growth stage that their crops or fields are in. Providing the estimated growth stage to a user can include : storing the estimated growth stage and/or growth phase in association with the user account; transmitting (e.g., wirelessly or through a wire) and/or displaying the estimated growth stage and/or growth phase to a user device associated with the user accoun t.” Therefore, claims 1-8 are rejected under 35 USC 102. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to RICHARD W. CRANDALL whose telephone number is (313)446-6562. The examiner can normally be reached M - F, 8:00 AM - 5:00 PM. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at (571) 270-3614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /RICHARD W. CRANDALL/ Primary Examiner, Art Unit 3619 Application/Control Number: 18/400,160 Page 2 Art Unit: 3619 Application/Control Number: 18/400,160 Page 3 Art Unit: 3619 Application/Control Number: 18/400,160 Page 4 Art Unit: 3619 Application/Control Number: 18/400,160 Page 5 Art Unit: 3619 Application/Control Number: 18/400,160 Page 6 Art Unit: 3619 Application/Control Number: 18/400,160 Page 7 Art Unit: 3619 Application/Control Number: 18/400,160 Page 8 Art Unit: 3619 Application/Control Number: 18/400,160 Page 9 Art Unit: 3619 Application/Control Number: 18/400,160 Page 10 Art Unit: 3619 Application/Control Number: 18/400,160 Page 11 Art Unit: 3619 Application/Control Number: 18/400,160 Page 12 Art Unit: 3619 Application/Control Number: 18/400,160 Page 13 Art Unit: 3619 Application/Control Number: 18/400,160 Page 14 Art Unit: 3619 Application/Control Number: 18/400,160 Page 15 Art Unit: 3619