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 .
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,” should be avoided.
The abstract of the disclosure is objected to because its length exceeds 150 words. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
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 data sources, the source data including health data for the herd of livestock; and normalize the source data received from each data source of the plurality of data sources to link the source data from each of the data sources for a specified livestock of the herd and generate the livestock data; creating, by the processor, a data model based on the livestock data, the data model identifying a trend in the health data of the herd of livestock; generating, by the processor, based on the data model and the identified trend, a task for managing the herd of livestock; and outputting the task.
The Examiner submits that the foregoing underlined limitations constitute “a mental process” because receiving livestock data; receive source data, the source data including health data for the herd of livestock; normalize the source data to link the source data for a specified livestock of the herd and generate the livestock data; creating a data model based on the livestock data, the data model identifying a trend in the health data of the herd of livestock; generating, based on the data model and the identified trend, a task for managing the herd of livestock 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, creating 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 wherein the health data includes cow calendar data for each livestock of the herd; wherein the generated task is a treatment plan for one or more specific livestock of the herd or a vaccine administration timeline; 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 production data and the data model includes a model of milk production on the 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 the 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 selected geographical area 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 the herd; wherein the treatment regime includes a suggestion of a drug to administer to the 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 the farm; wherein the treatment regime includes an inventory of the drug on the 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 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 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 10, 12, 16, 17, 18, and 19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6, 18, and 19 recite the limitation "the farm" in line 2 of claim 6, line 2 of claim 18, and line 2 of claim 19. There is insufficient antecedent basis for this limitation in the claims.
Claim 6 recites “production data” at line 1. it is unclear if this is the same “production data” recited in claim 1, or different.
Claims 16, 17, and 18 recite the limitation "the treatment regime" in line 1. There is insufficient antecedent basis for this limitation in the claims.
Claims 17 and 18 recite the limitation "the drug" in line 2. There is insufficient antecedent basis for this limitation in the claims.
Claim 16 recites the limitation "the illness" in line 2. There is insufficient antecedent basis for this limitation in the claim. It appears claim 16 should depend on claim 15, not claim 1.
Claim 12 recites the limitation "the specified livestock data" in line 1. There is insufficient antecedent basis for this limitation in the claim. It appears claim 12 should depend on claim 11, not claim 1.
Claim 10 recites the limitations "the user" and “the video instructions, the audio instructions and/or the written instructions” in lines 1-3. There is insufficient antecedent basis for these limitations in the claim. It appears claim 10 should depend on claim 8, not claim 7.
Claim Objections
Claim 11 is objected to because of the following informalities: change “the selected geographical area” to “the one or more selected geographical areas“ in the last step of the claim. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 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 –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 6-9, 12, 15, 16, and 18-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Kuper et al. (US 2023/0153693 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 data sources, 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 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.); and
normalize the source data received from each data source of the plurality of data sources to link the source data from each of the data sources for a specified livestock of the herd and generate the livestock data (para. 23, 24, 26, and 30 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);
create a data model based on the livestock data, the data model identifying a trend in the health data of the herd of livestock (para. 40-42 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.);
generate, based on the data model and the identified trend, a task for managing the herd of livestock (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 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.).
(B) Referring to claim 2, Kuper discloses wherein the health data includes cow calendar data for each livestock of the herd (para. 42, 43, and 50 of Kuper).
(C) Referring to claim 3, Kuper discloses wherein the generated task is a treatment plan for one or more specific livestock of the herd or a vaccine administration timeline (para. 46 & 47 of Kuper).
(D) Referring to claim 4, Kuper discloses wherein the 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).
(E) Referring to claim 6, Kuper discloses wherein the livestock data includes production data and the data model includes a model of milk production on the farm (para. 28, 29, and 42 of Kuper).
(F) Referring to claim 7, Kuper discloses wherein the processor is further configured to, after generating the task for managing the herd of livestock, generating a protocol for completing the task (para. 39, 42, and 47 of Kuper).
(G) Referring to claim 8, Kuper discloses wherein the protocol includes video instructions, audio instructions and/or written instructions for completing the task (para. 39, 42, and 47 of Kuper).
(H) 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).
(I) 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).
(J) Referring to claim 15, Kuper discloses wherein the task includes a treatment regime for an illness of the herd, the illness being identified based on the identified trend (para. 42, 43, 46, and 47 of Kuper).
(K) Referring to claim 16, Kuper discloses wherein the treatment regime includes a suggestion of a drug to administer to the 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).
(L) Referring to claim 18, Kuper discloses wherein the treatment regime includes an inventory of the drug on the farm (para. 43 & 6 of Kuper).
(M) Referring to claim 19, Kuper discloses further comprising a display device centrally located on the farm, the display device presenting the task to be performed to manage the herd of livestock (para. 8, 10, 24, 41, 19, and 20 of Kuper).
(N) 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 data sources, the source data including health data for the herd of livestock (Fig. 1 and para. 20, 21, & 26-28 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.); and
normalize the source data received from each data source of the plurality of data sources to link the source data from each of the data sources for a specified livestock of the herd and generate the livestock data (para. 23, 24, 26, and 30 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);
creating, by the processor, a data model based on the livestock data, the data model identifying a trend in the health data of the herd of livestock (para. 40-42 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.);
generating, by the processor, based on the data model and the identified trend, a task for managing the herd of livestock (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 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.).
Claim Rejections - 35 USC § 103
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) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (US 2023/0153693 A1) in view of Park (WO 2019/216647 A1).
(A) Referring to claim 5, Kuper does 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. 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 Herman (WO 2014/066895 A2).
(A) Referring to claim 10, Kuper does not disclose wherein the protocol is at least partially provided by the 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 task.
Herman discloses wherein the protocol is at least partially provided by the 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 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. The motivation for doing so would have been to have pertinent information in an account (para. 21 of Herman).
Claim(s) 11, 13, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuper et al. (US 2023/0153693 A1) in view of O’Hare (US 2010/0107985 A1).
(A) Referring to claim 11, Kuper does 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 selected geographical area 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 selected geographical area 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. 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 13, Kuper does 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. The motivation for doing so would have been to include all relevant dimensions and features (para. 24 of O’Hare).
(C) Referring to claim 14, Kuper does 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. 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 O’Hare (US 2010/0107985 A1), and further in view of Dunlop (US 2016/0246934 A1).
(A) Referring to claim 17, Kuper and O’Hare do not disclose wherein the treatment regime includes a dosage route of the drug and/or a physical location of the drug on the farm.
Dunlop discloses wherein the treatment regime includes a dosage route of the drug and/or a physical location of the drug on the 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 O’Hare. The motivation for doing so would have been to include protocol instructions (para. 143 & 145 of Dunlop).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The cited but not applied prior art teaches optical livestock counting system and method (US 2016/0363692 A1); and method for managing dairy production (US 2014/0116341 A1).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LENA NAJARIAN whose telephone number is (571)272-7072. The examiner can normally be reached Monday - Friday 9:30 am-6 pm.
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/LENA NAJARIAN/Primary Examiner, Art Unit 3687