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 .
Notice to Applicant
This communication is in response to the amendment filed 7/2/26. Claims 1-4, 6-8, 10-12, and 15-20 have been amended. Claims 1-20 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
Claims 20 is directed to a method (i.e., a process) and claims 1-19 are directed to a system (i.e., a machine). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts.
Representative independent claim 20 includes limitations that recite at least one abstract idea. Specifically, independent claim 20 recites:
20. A computer implemented method of managing a herd of livestock, the method comprising:
receiving, at a processor, livestock data from a herd management software system, the herd management system configured to:
receive source data from each data source of a plurality of third-party data sources that operate in a disconnected fashion, the source data including health data for the herd of livestock; and
normalize and cleanse the source data received from each data source of the plurality of third-party data sources that operate in a disconnected fashion to link the source data from each of the third-party data sources for a specified livestock of the herd and generate the livestock data;
executing, by the processor, a predictive livestock data model based on the livestock data, the predictive livestock data model establishing a baseline for the specified livestock, a group of livestock of the herd, or the herd and identifying a departure from the baseline indicative of a predicted livestock event;
automatically generating, by the processor, based on the predictive livestock data model and the identified departure, a herd management task specifying an action to be performed for managing the specified livestock, the group of livestock of the herd, or the herd; and
outputting the herd management task.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because receive source data, the source data including health data for the herd of livestock; and normalize and cleanse the source data to link the source data for a specified livestock of the herd and generate the livestock data; executing a predictive livestock data model based on the livestock data, the predictive livestock data model establishing a baseline for the specified livestock, a group of livestock of the herd, or the herd and identifying a departure from the baseline indicative of a predicted livestock event; generating, based on the predictive livestock data model and the identified departure, a herd management task specifying an action to be performed for managing the specified livestock, the group of livestock of the herd, or the herd amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper.
Accordingly, the claim recites at least one abstract idea.
Step 2A - Prong Two:
Regarding Prong Two of Step 2A, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The limitations of claims 1 and 20, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting a processor, software system, data sources, and a memory to perform the limitations, nothing in the claim elements precludes the steps from practically being performed in the mind. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the processor, software system, data sources, and memory are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of receiving data, normalizing data, cleansing data, executing a data model, generating data, and outputting data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (see MPEP § 2106.05). Their collective functions merely provide conventional computer implementation.
Claims 2-19 are ultimately dependent from Claim(s) 1 and include all the limitations of Claim(s) 1. Therefore, claim(s) 2-19 recite the same abstract idea. Claims 2-19 describe further limitations regarding associate the task with a tool; generate and associate the task; wherein the data model includes a personalized timeline for each livestock in the herd; wherein the personalized timeline includes one or more dates of predicted events, the predicted events including one or more of a date of giving birth and/or a lactation period start date and/or a lactation period end date, the predicted events being determined based on the health data; wherein the livestock data includes the production data and the data model includes a model of milk production on a farm; generating a protocol for completing the task; wherein the protocol includes video instructions, audio instructions and/or written instructions for completing the task; wherein the protocol is pre-populated; wherein the protocol is at least partially provided by a user; receive image data depicting at least a portion of a farm; receive a user input indicating a boundary of one or more selected geographical areas of the farm; receive a selection of one or more livestock of the herd, assign the one or more livestock to the one or more selected geographical areas of the farm; wherein the specified livestock data includes location data providing a geographical location of the livestock; types of image data; display the specified livestock data depicting the boundary on the image data; wherein the task includes a treatment regime for an illness of specified livestock of the herd; wherein the treatment regime includes a suggestion of a drug to administer to the specified livestock to treat the illness; wherein the treatment regime includes a dosage route of the drug and/or a physical location of the drug on a farm; wherein the treatment regime includes an inventory of the drug on a farm; and presenting the task to be performed to manage the herd of livestock. These are all just further describing the abstract idea recited in claim 1, without adding significantly more.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Step 2B:
Regarding Step 2B, independent claims 1 and 20 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
Regarding the additional limitations directed to a processor receiving data from a software system and the software system receiving data from a plurality of third-party data sources, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of receiving and transmitting data over a network. See MPEP 2106.05(d)(II).
The dependent claims do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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.
Claim 3 is 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.
The newly added recitation of "wherein the processor is further configured to generate a task and associate the task with a scheduled time for performance, wherein the scheduled time is determined based on predefined rules stored in the memory, the predefined rules including at least one of a time-based threshold or a priority level" within claim 3 appears to constitute new matter.
In particular, Applicant does not point to, nor was the Examiner able to find support for this newly added language within the specification as originally filed. As such, Applicant is respectfully requested to clarify the above issues and to specifically point out support for the newly added limitations in the originally filed specification and claims.
Applicant is required to cancel the new matter in the reply to this Office Action.
Claim Objections
Claim 7 is objected to because of the following informalities: change “…the herd of livestock…” to “…the herd of specified livestock….” Appropriate correction is required.
Claim 15 is objected to because of the following informalities: change “specified livestock“ to “the specified livestock.“ Appropriate correction is required.
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.
Claim(s) 1, 4, 6-9, 15, 16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (US 2023/0153693 A1) in view of Han et al. (US 2021/0104335 A1).
(A) Referring to claim 1, Kuper discloses A system for managing a herd of livestock, the system comprising (abstract and para. 3 of Kuper; a system and method for tracking and managing livestock):
a memory (para. 56-58 of Kuper);
a processor in communication with the memory, the processor configured to (para. 56-58 of Kuper; systems and methods of the present invention may also be partially implemented in software that can be stored on a storage medium, executed on programmed general-purpose computer with the cooperation of a controller and memory, a special purpose computer, a microprocessor, or the like.):
receive livestock data from a herd management software system, the herd management system configured to (Fig. 1 and para. 20, 21, & 26-28 of Kuper; The data retrieval and initialization module 151 is also configured to ingest, receive, request, or otherwise obtain additional information that aids the framework 100 in processing the input data 110 collected from RFID tags 104, by augmenting livestock data and geographical data 111 with other data that is relevant to evaluating, modeling and diagnosing an animal condition 160. This additional information may include environmental data 117, regional data 120, nutrition data 123, regional animal-specific or model-specific data 124, producer-augmented data 129, and reader attributes 133, and regardless of its type, may include any information not temporally gathered directly or on site, such as for example market pricing (such as livestock commodities data for live cattle, feeder cattle, corn, and milk future prices), disease outbreaks in other geographies, etc.):
receive source data from each data source of a plurality of third-party data sources that operate in a disconnected fashion, the source data including at least one of health data, production data, growth data and genomic data for the herd of livestock (Fig. 1 and para. 20, 21, 26-28, 30, and 35 of Kuper; Regional data 120 may further include trend and diagnosis information for the region where a RFID tag 104 resides, or where livestock 102 are maintained. Such trend and diagnosis information may provide health information and forecasts for the livestock by region which may impact, growth and behavior going forward, and which may influence growth and dairy production modeling. The arrival and risk assessment may therefore provide the agricultural data collection framework 100 with a complete animal health history. It should be noted that this arrival and risk assessment data may be procured from many sources, such as directly from an RFID tag 104 itself, from a reference database maintained or stored separately, or provided by third party sources such as another user of the framework 100 or from a third party or separate system integrated with the framework 100.); and
format the source data received from each data source of the plurality of third-party data sources that operate in a disconnected fashion to link the source data from each of the third-party data sources for a specified livestock of the herd and generate the livestock data (para. 23, 24, 26, 30, and 35 of Kuper; This data retrieval and initialization module 151 may also be configured to condition or format raw input data 110 from the RFID tags 104, and from such additional sources, so as to be prepared for the artificial intelligence and machine learning 162 and statistical process control and change detection algorithms 152 aspects of the framework 100. In the agricultural data collection and processing framework 100 of the present invention, information obtained by the UHF readers 150 from the RFID tags 104 may also include geographical information 111, which correlates the livestock information in a RFID tag 104 with location data. Data about livestock 102 may therefore be geo-tagged with information identifying a region 112, a feedlot 113, a pen 114, a farm 115, or any other type of enclosure or location where livestock 102 are maintained. The data retrieval and initialization module 151 is also configured to ingest, receive, request, or otherwise obtain additional information that aids the framework 100 in processing the input data 110 collected from RFID tags 104, by augmenting livestock data and geographical data 111 with other data that is relevant to evaluating, modeling and diagnosing an animal condition 160. This additional information may include environmental data 117, regional data 120, nutrition data 123, regional animal-specific or model-specific data 124. It should be noted that this arrival and risk assessment data may be procured from many sources, such as directly from an RFID tag 104 itself, from a reference database maintained or stored separately, or provided by third party sources such as another user of the framework 100 or from a third party or separate system integrated with the framework 100.);
execute a predictive livestock data model based on the livestock data, the predictive livestock data model establishing a baseline for the specified livestock, a group of livestock of the herd, or the herd and identifying a departure from the baseline indicative of a predicted livestock event (para. 40-42 & 46 of Kuper; The profile of the animal condition 160, and livestock tracking and management characteristics 172 therein, may be generated as output data 170 as discussed further below, and may also be provided back to the machine learning base models 162 and used to adjust and/or train a base model 168. Output data 170 may also include specific information derived from the livestock tracking and management characteristics 172, predictions 174 and alerts 176, such as for example a pre-diagnosis of health issues 182, identification of disease trends 183, peak livestock weights 184, behavioral patterns 185 (for example, grazing behavior suggestive of inadequate pasture), and indications of specific health events 186, such as calving 187, estrus 188, and injury 189. The output data 170 may further be processed to identify environmental interactions 190 that affect other livestock models, such as growth models and dairy production models. The one or more machine learning base models 162 takes these inputs develops correlations and weights 164 to generate the weighted vector space of learning data 166 based on any actual, historical producer-specified treatments provided for the animal condition 160. This learning process is followed by a real-time predictive analysis performed by the statistical process controls and change detection algorithms 152 to identify livestock tracking and management characteristics 172 where there is a possible deviation from a normality 156 for a particular geographical location (such as the feedlot 113) to identify sick livestock before they show any visual signs of stress or illness or otherwise become in need of treatment.);
automatically generate, based on the predictive livestock data model and the identified departure, a herd management task specifying an action to be performed for managing the specified livestock, the group of livestock of the herd, or the herd (par. 40-42, 19, 46, & 47 of Kuper; Outputs from the framework, whether in the form of predictions, alerts, or otherwise, assist in allocating and prioritization usage of resources for livestock tracking and management. Further, the present invention allows producers of livestock to ensure that animals receive the diet, nutrition, health supplements, and veterinary care needed in response to such predictions and/or allocations and prioritizations. The learning data based on the actual producer treatment data is used in real time to predict animals with behaviors that would also lead to producer treatments of the current livestock 102. These animals would be identified for the producer to do a “pre-check” health determination, allowing the producer to possibly prevent further outbreak or animal death.); and
output the herd management task (para. 41, 19, and 46 of Kuper; The livestock tracking and management characteristics 172, predictions 174 and alerts 176 may be provided to users via a display, such as a graphical user interface, interactive or otherwise, for example via a support tool or other mechanism.).
Kuper does not expressly disclose normalize and cleanse the source data.
Han discloses normalize and cleanse the source data (para. 45, 91, and 130 of Han; the LMS unit 130 may be provided with the big data analysis module for real-time monitoring to perform real-time data collection, storage, and processing, in which the LMS unit 130 may collect a large amount of scanning data and sensor state information, perform data cleansing, normalization, and verification on the collected data, perform normalization and preprocessing to efficiently process massive data, perform preprocessing on the moving route and the data of the moving object, and extract descriptive statistics of the preprocessed data to obtain real-time route analysis data and moving route prediction data. ).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Han within Kuper. The motivation for doing so would have been to efficiently process massive data (para. 45 of Han).
(B) Referring to claim 4, Kuper discloses wherein the predictive livestock data model includes a personalized timeline for each livestock in the herd, the personalized timeline including past events during the life of the livestock including at least one of: birth date, date bred, date of lameness, date of foot trim, date of pregnancy, and date of vaccination (para. 30 and 33 of Kuper).
(C) Referring to claim 6, Kuper discloses wherein the livestock data includes the production data and the predictive livestock data model includes a model of milk production on a farm (para. 28, 29, and 42 of Kuper).
(D) Referring to claim 7, Kuper discloses wherein the processor is further configured to, after automatically generating the herd management task to be performed for managing the herd of livestock, generating a protocol for completing the herd management task (para. 39, 42, and 47 of Kuper).
(E) Referring to claim 8, Kuper discloses wherein the protocol includes video instructions, audio instructions and/or written instructions for completing the herd management task (para. 39, 42, and 47 of Kuper).
(F) Referring to claim 9, Kuper discloses wherein the protocol is pre-populated by an operator of the processor (para. 47, 41, and 30 of Kuper).
(G) Referring to claim 15, Kuper discloses wherein the herd management task includes a treatment regime for an illness of specified livestock of the herd, the illness being identified based on the departure (para. 42, 43, 46, and 47 of Kuper).
(H) Referring to claim 16, Kuper discloses wherein the treatment regime includes a suggestion of a drug to administer to the specified livestock to treat the illness, the suggestion based on the health data, the health data including at least one of: a temperature of the livestock, a weight of the livestock, a gender of the livestock and an age of the livestock (para. 30, 37, 43, and 46 of Kuper).
(I) Referring to claim 18, Kuper discloses wherein the treatment regime includes an inventory of the drug on a farm (para. 43 & 6 of Kuper).
(J) Referring to claim 19, Kuper discloses further comprising a display device centrally located on a farm, the display device presenting the herd management task to be performed to manage the herd of livestock (para. 8, 10, 24, 41, 19, and 20 of Kuper).
(K) Referring to claim 20, Kuper discloses A computer implemented method of managing a herd of livestock, the method comprising (abstract and para. 3 of Kuper; a system and method for tracking and managing livestock):
receiving, at a processor, livestock data from a herd management software system, the herd management system configured to (Fig. 1 and para. 20, 21, & 26-28 of Kuper; The data retrieval and initialization module 151 is also configured to ingest, receive, request, or otherwise obtain additional information that aids the framework 100 in processing the input data 110 collected from RFID tags 104, by augmenting livestock data and geographical data 111 with other data that is relevant to evaluating, modeling and diagnosing an animal condition 160. This additional information may include environmental data 117, regional data 120, nutrition data 123, regional animal-specific or model-specific data 124, producer-augmented data 129, and reader attributes 133, and regardless of its type, may include any information not temporally gathered directly or on site, such as for example market pricing (such as livestock commodities data for live cattle, feeder cattle, corn, and milk future prices), disease outbreaks in other geographies, etc.):
receive source data from each data source of a plurality of third-party data sources that operate in a disconnected fashion, the source data including health data for the herd of livestock (Fig. 1 and para. 20, 21, 26-28, 30, and 35 of Kuper; Regional data 120 may further include trend and diagnosis information for the region where a RFID tag 104 resides, or where livestock 102 are maintained. Such trend and diagnosis information may provide health information and forecasts for the livestock by region which may impact, growth and behavior going forward, and which may influence growth and dairy production modeling. The arrival and risk assessment may therefore provide the agricultural data collection framework 100 with a complete animal health history. It should be noted that this arrival and risk assessment data may be procured from many sources, such as directly from an RFID tag 104 itself, from a reference database maintained or stored separately, or provided by third party sources such as another user of the framework 100 or from a third party or separate system integrated with the framework 100.); and
format the source data received from each data source of the plurality of third-party data sources that operate in a disconnected fashion to link the source data from each of the third-party data sources for a specified livestock of the herd and generate the livestock data (para. 23, 24, 26, 30, and 35 of Kuper; This data retrieval and initialization module 151 may also be configured to condition or format raw input data 110 from the RFID tags 104, and from such additional sources, so as to be prepared for the artificial intelligence and machine learning 162 and statistical process control and change detection algorithms 152 aspects of the framework 100. In the agricultural data collection and processing framework 100 of the present invention, information obtained by the UHF readers 150 from the RFID tags 104 may also include geographical information 111, which correlates the livestock information in a RFID tag 104 with location data. Data about livestock 102 may therefore be geo-tagged with information identifying a region 112, a feedlot 113, a pen 114, a farm 115, or any other type of enclosure or location where livestock 102 are maintained. The data retrieval and initialization module 151 is also configured to ingest, receive, request, or otherwise obtain additional information that aids the framework 100 in processing the input data 110 collected from RFID tags 104, by augmenting livestock data and geographical data 111 with other data that is relevant to evaluating, modeling and diagnosing an animal condition 160. This additional information may include environmental data 117, regional data 120, nutrition data 123, regional animal-specific or model-specific data 124. It should be noted that this arrival and risk assessment data may be procured from many sources, such as directly from an RFID tag 104 itself, from a reference database maintained or stored separately, or provided by third party sources such as another user of the framework 100 or from a third party or separate system integrated with the framework 100.);
executing, by the processor, a predictive livestock data model based on the livestock data, the predictive livestock data model establishing a baseline for the specified livestock, a group of livestock of the herd, or the herd and identifying a departure from the baseline indicative of a predicted livestock event (para. 40-42 & 46 of Kuper; The profile of the animal condition 160, and livestock tracking and management characteristics 172 therein, may be generated as output data 170 as discussed further below, and may also be provided back to the machine learning base models 162 and used to adjust and/or train a base model 168. Output data 170 may also include specific information derived from the livestock tracking and management characteristics 172, predictions 174 and alerts 176, such as for example a pre-diagnosis of health issues 182, identification of disease trends 183, peak livestock weights 184, behavioral patterns 185 (for example, grazing behavior suggestive of inadequate pasture), and indications of specific health events 186, such as calving 187, estrus 188, and injury 189. The output data 170 may further be processed to identify environmental interactions 190 that affect other livestock models, such as growth models and dairy production models. The one or more machine learning base models 162 takes these inputs develops correlations and weights 164 to generate the weighted vector space of learning data 166 based on any actual, historical producer-specified treatments provided for the animal condition 160. This learning process is followed by a real-time predictive analysis performed by the statistical process controls and change detection algorithms 152 to identify livestock tracking and management characteristics 172 where there is a possible deviation from a normality 156 for a particular geographical location (such as the feedlot 113) to identify sick livestock before they show any visual signs of stress or illness or otherwise become in need of treatment.);
automatically generating, by the processor, based on the predictive livestock data model and the identified departure, a herd management task specifying an action to be performed for managing the specified livestock, the group of livestock of the herd, or the herd (para. 40-42, 19, 46, & 47 of Kuper; Outputs from the framework, whether in the form of predictions, alerts, or otherwise, assist in allocating and prioritization usage of resources for livestock tracking and management. Further, the present invention allows producers of livestock to ensure that animals receive the diet, nutrition, health supplements, and veterinary care needed in response to such predictions and/or allocations and prioritizations. The learning data based on the actual producer treatment data is used in real time to predict animals with behaviors that would also lead to producer treatments of the current livestock 102. These animals would be identified for the producer to do a “pre-check” health determination, allowing the producer to possibly prevent further outbreak or animal death.); and
outputting the herd management task (para. 41, 19, and 46 of Kuper; The livestock tracking and management characteristics 172, predictions 174 and alerts 176 may be provided to users via a display, such as a graphical user interface, interactive or otherwise, for example via a support tool or other mechanism.).
Kuper does not expressly disclose normalize and cleanse the source data.
Han discloses normalize and cleanse the source data (para. 45, 91, and 130 of Han; the LMS unit 130 may be provided with the big data analysis module for real-time monitoring to perform real-time data collection, storage, and processing, in which the LMS unit 130 may collect a large amount of scanning data and sensor state information, perform data cleansing, normalization, and verification on the collected data, perform normalization and preprocessing to efficiently process massive data, perform preprocessing on the moving route and the data of the moving object, and extract descriptive statistics of the preprocessed data to obtain real-time route analysis data and moving route prediction data. ).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Han within Kuper. The motivation for doing so would have been to efficiently process massive data (para. 45 of Han).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (US 2023/0153693 A1) in view of Han et al. (US 2021/0104335 A1), and further in view of Park (WO 2019/216647 A1).
(A) Referring to claim 5, Kuper and Han do not disclose wherein the personalized timeline includes one or more dates of predicted events, the predicted events including one or more of a date of giving birth and/or a lactation period start date and/or a lactation period end date, the predicted events being determined based on the health data.
Park discloses wherein the personalized timeline includes one or more dates of predicted events, the predicted events including one or more of a date of giving birth and/or a lactation period start date and/or a lactation period end date, the predicted events being determined based on the health data (see page 3 of Park; Predicting the amount of milk produced. The state information data of the cow to be managed includes the birth date or month of the cow to be managed and postpartum parking at the specific date, and the nutritional intake data of the cow to be managed includes the daily building intake, water intake, Metabolic energy intake, metabolic protein intake, MET intake, LYS intake, calcium intake, and phosphorus intake, and the ambient state data includes average temperature and average humidity information for the specific date. A milk production history data of the corresponding cow, wherein the status information data of the corresponding cow includes the birth date or month of the corresponding cow and postpartum parking at the reference day, and the nutrition intake data of the corresponding cow is a daily building on the reference day.).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Park within Kuper and Han. The motivation for doing so would have been to manage a target cow (abstract of Park).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (US 2023/0153693 A1) in view of Han et al. (US 2021/0104335 A1), and further in view of Herman (WO 2014/066895 A2).
(A) Referring to claim 10, Kuper and Han do not disclose wherein the protocol is at least partially provided by a user by uploading at least one of the video instructions, the audio instructions and/or the written instructions to the processor before the processor generates the herd management task.
Herman discloses wherein the protocol is at least partially provided by a user by uploading at least one of the video instructions, the audio instructions and/or the written instructions to the processor before the processor generates the herd management task (para. 21 & 51 of Herman).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Herman within Kuper and Han. The motivation for doing so would have been to have pertinent information in an account (para. 21 of Herman).
Claim(s) 11-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (US 2023/0153693 A1) in view of Han et al. (US 2021/0104335 A1), and further in view of O’Hare (US 2010/0107985 A1).
(A) Referring to claim 11, Kuper and Han do not disclose wherein the processor is further configured to: receive image data depicting at least a portion of a farm; receive a user input indicating a boundary of one or more selected geographical areas of the farm; receive a selection of one or more livestock of the herd, the one or more livestock having specified livestock data associated therewith stored on the memory; and assign the one or more livestock to the one or more selected geographical areas of the farm.
O’Hare discloses wherein the processor is further configured to: receive image data depicting at least a portion of a farm (Fig. 1, para. 20 & 24 of O’Hare); receive a user input indicating a boundary of one or more selected geographical areas of the farm (para. 4-7, 15, 19, and 26 of O’Hare); receive a selection of one or more livestock of the herd, the one or more livestock having specified livestock data associated therewith stored on the memory (para. 20 of O’Hare); and assign the one or more livestock to the one or more selected geographical areas of the farm (para. 20 & 26 of O’Hare).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of O’Hare within Kuper and Han. The motivation for doing so would have been to identify and track the movements of animals within a monitoring zone (abstract of O’Hare).
(B) Referring to claim 12, Kuper discloses wherein the specified livestock data includes location data providing a geographical location of the livestock (para. 8, 9, and 24 of Kuper).
(C) Referring to claim 13, Kuper and Han do not disclose wherein the image data is one of an aerial image, an AutoCAD drawing and mapping data received from a third party data source.
OHare discloses wherein the image data is one of an aerial image, an AutoCAD drawing and mapping data received from a third party data source (para. 24 of O’Hare)
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of O’Hare within Kuper and Han. The motivation for doing so would have been to include all relevant dimensions and features (para. 24 of O’Hare).
(D) Referring to claim 14, Kuper and Han do not disclose wherein the processor is further configured to display the specified livestock data on a user interface depicting the boundary on the image data, the specified livestock data overlaying the image data within the boundary.
O’Hare discloses wherein the processor is further configured to display the specified livestock data on a user interface depicting the boundary on the image data, the specified livestock data overlaying the image data within the boundary (Fig. 1, para. 12, 20, 26-28, and 4-6 of O’Hare).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of O’Hare within Kuper and Han. The motivation for doing so would have been so that results can be easily accessed by the farm manager (para. 28 of O’Hare).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (US 2023/0153693 A1) in view of Han et al. (US 2021/0104335 A1), and further in view of Dunlop (US 2016/0246934 A1).
(A) Referring to claim 17, Kuper and Han do not disclose wherein the treatment regime includes a dosage route of the drug and/or a physical location of the drug on a farm.
Dunlop discloses wherein the treatment regime includes a dosage route of the drug and/or a physical location of the drug on a farm (para. 143-145 & 153 of Dunlop).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Dunlop within Kuper and Han. The motivation for doing so would have been to include protocol instructions (para. 143 & 145 of Dunlop).
Claim(s) 2 and 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (US 2023/0153693 A1) in view of Han et al. (US 2021/0104335 A1), and further in view of Beverly (US 2023/0178196 A1).
(A) Referring to claim 2, Kuper and Han do not expressly disclose wherein the processor is further configured to associate the herd management task with a calendar management tool.
Beverly discloses wherein the processor is further configured to associate the herd management task with a calendar management tool (para. 68, 75, 110, 179, and 161 of Beverly).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned feature of Beverly within Kuper and Han. The motivation for doing so would have been to arrange for an entity to complete tasks (para. 109 of Beverly).
(B) Referring to claim 3, Kuper and Han do not disclose wherein the processor is further configured to generate a task and associate the task with a scheduled time for performance, wherein the scheduled time is determined based on predefined rules stored in the memory, the predefined rules including at least one of a time-based threshold or a priority level.
Beverly discloses wherein the processor is further configured to generate a task and associate the task with a scheduled time for performance, wherein the scheduled time is determined based on predefined rules stored in the memory, the predefined rules including at least one of a time-based threshold or a priority level (para. 94, 109, 110, 116, 144, and 163-165 of Beverly).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the aforementioned features of Beverly within Kuper and Han. The motivation for doing so would have been to display appointment details (para. 162 of Beverly).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's additional arguments filed 7/2/26 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 7/2/26.
(1) Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 101.
(2) Applicant respectfully requests withdrawal of the rejection under 35 U.S.C. § 102.
(A) As per the first argument, see 101 rejection above. The Examiner submits that the foregoing underlined limitations in the 101 rejection above constitute “a mental process” because receive source data, the source data including health data for the herd of livestock; and normalize and cleanse the source data to link the source data for a specified livestock of the herd and generate the livestock data; executing a predictive livestock data model based on the livestock data, the predictive livestock data model establishing a baseline for the specified livestock, a group of livestock of the herd, or the herd and identifying a departure from the baseline indicative of a predicted livestock event; generating, based on the predictive livestock data model and the identified departure, a herd management task specifying an action to be performed for managing the specified livestock, the group of livestock of the herd, or the herd amount to observations/evaluations/judgments/analyses that can, at the currently claimed high level of generality, be practically performed in the human mind or via pen and paper. Accordingly, the claim recites at least one abstract idea. This judicial exception is not integrated into a practical application. In particular, the processor, software system, data sources, and memory are recited at a high-level of generality (i.e., as generic computer components performing generic computer functions of receiving data, normalizing data, cleansing data, executing a data model, generating data, and outputting data) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Regarding the additional limitations directed to a processor receiving data from a software system and the software system receiving data from a plurality of third-party data sources, all of which the Examiner submits merely add insignificant extra-solution activity to the abstract idea or are claimed in a merely generic manner (e.g., at a high level of generality), the Examiner further submits that such steps are not unconventional as they merely consist of receiving and transmitting data over a network. See MPEP 2106.05(d)(II). Applicant’s arguments regarding a technical improvement are not persuasive. For example, Applicant’s arguments regarding paragraph 76 of the Specification are not persuasive because “using accurate information” is not a technical improvement. Additional arguments discuss generic computer functions, as stated above.(B) As per the second argument, see newly added Han reference. Applicant’s arguments regarding the predictive livestock data model are not persuasive. For example, see paragraph 46 of Kuper which states: “The one or more machine learning base models 162 takes these inputs develops correlations and weights 164 to generate the weighted vector space of learning data 166 based on any actual, historical producer-specified treatments provided for the animal condition 160. This learning process is followed by a real-time predictive analysis performed by the statistical process controls and change detection algorithms 152 to identify livestock tracking and management characteristics 172 where there is a possible deviation from a normality 156 for a particular geographical location (such as the feedlot 113) to identify sick livestock before they show any visual signs of stress or illness or otherwise become in need of treatment.” Regarding generating the task, see para. 40-42, 19, 46, & 47 of Kuper. As such, it is unclear how the language of the claim differs from the applied prior art. Note that the claim does not recite the type of “task” or “action.”
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.
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687
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