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 .
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, 4 –7, 9, 12, 14, 16, 19 – 22, 24, 25, 27, 29, and 31 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) processing circuitry configured to perform certain functions, corresponding methods, and a computer readable medium containing the corresponding executable code thereon (which are each within a statutory category of invention) to “analyze” received data and “predict” based on the analysis, which falls in the category of a mental process (see MPEP 2106.04(a)(2)III.). These judicial exceptions are not integrated into a practical application because with regard to Revised step 2A, an exception is present as noted, and with regard to Revised step 2B, the claim does not recite additional elements that integrate the judicial exception into a practical application. In particular, based on the high level of generality/nominal nature of “provide a monitored record” (Examiner notes that the method is performed by a computer accessing data, but the sensor structures do not provide positively claimed details) and “notify a user” to providing an output of the results of use of the judicial exception, one must conclude that these recitations do not impose a meaningful limitation onto the claim scope, as the limitations do not constitute use of the exception in the context of “a particular machine”. Instead, their high level of generality merely points to a generalized pre-processing data gathering and a post-processing data outputting being undertaken. Likewise, the claim(s) does/do not include additional elements/steps that are sufficient to amount to significantly more than the judicial exception because the high level and broad renditions regarding any sensors and generic providing of an indication indicate that no specific sensors or outputting devices are required. Further, the dependent claims generally relate to further aspects of the judicial exceptions, and thus also fail to provide details to integrate the exceptions into a practical application.
Examiner notes that in contrast to the originally recited details of claim 2 (which was not rejected under 35 USC 101 in the Non-final rejection mailed January 27, 2026) setting forth certain particular details of the machine learning implementation, the amended claims merely refer to a generic/high Level use of ML without bounds. Applicant is referred to and reminded that consistent with the analysis of Claim 2 in 101 Guidance Example 47, use of mere broad reference to ML is understood as encompassing the corresponding mental processes that the processing attempts to capture, and does not serve to exempt the claim scope from the underlying Abstract ideas by integrating the exceptions into a practical application or otherwise avoiding the conclusion of ineligible subject matter.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1, 6, 7, 12, 14, 16, 21, 22, 27, 29, and 31 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Singh et al. (USPGPub 2019/0053470 – previously cited by Applicant).
Singh et al. discloses a system for predicting an illness, death or other abnormal condition of a monitored animal (a system for tracking, analyzing, and diagnosing the health of an individual animal or an animal population and for communicating the likelihood of illness of one or more members of the given animal population; paragraph [0027]), the system comprising a processing circuitry configured to:
provide a monitored record for the monitored animal (processor 130 to receive raw data from one or more tag assemblies 102 of the animal population; paragraph [0055]), the monitored record including a monitored temperature time series of monitored temperature values that are indicative of a temperature of the monitored animal over a given time period (the raw data comprises temperatures for a given time period; paragraphs [0056], [0079], [00801);
analyze the monitored temperature time-series (the temperature readings are analyzed; paragraph [0071]);
predict the illness, death or other abnormal condition of the monitored animal within a given time duration of the given time period, based on the analysis (determine if an animal is healthy, sick, diseased, or showing early warning signs of sickness based on analysis of the temperature data; paragraph [0071]); and
notify a user of the system of the prediction (tracking, analyzing, and diagnosing the health of an individual animal or an animal population and for communicating the likelihood of illness of one or more members of the given animal population; paragraph [0027]).
Further, Singh et al. discloses wherein the processing circuitry is configured to analyze the monitored temperature time series using a Machine Learning "ML" model (the temperatures are analyzed using a machine learning model; paragraph [0078]), the ML model being trained based on a data repository of historical records for a plurality of animals (train a machine learning classifier using datasets taken from animals with known physiological and behavioral characteristics; paragraphs [0066], [0182]), each historical record of the historical records including: A) a historical temperature time series of historical temperature values that are indicative of the temperature of a respective animal of the plurality of animals over an earlier time period (temperature readings obtained from animals with known bacterial infections may be used to train the machine learning classifier; wherein the data is obtained over a given time period; paragraphs [0171], [0172], [0182]), being earlier than and of an identical duration to the given time period (the animal's average historical temperature readings are determined as an average for the same selected time period for which the one or more temperature readings are being collected; paragraphs [0193]-[0194]), and B) a target field that indicates whether the respective animal became ill, died, or developed any other abnormal condition within the given time duration of the earlier time period (temperature readings obtained from animals with known bacterial infections may be used to train the machine learning classifier; paragraph [0182]).
Once trained, the machine learning classifier may be used for determining a health state of an animal (Figure 3A, 312; paragraph [0183]-[0185]).
Using the system in its intended manner, one would necessarily perform the method steps of Claim 16 and be in possession of a storage medium comprising the corresponding executable code of Claim 31.
Singh et al. recognize that data may be collected and analyzed over a variety of time periods, from hours, to a day, to ten days (paragraphs [0069]; [0185]).
As per claim 12 and parallel claim 27, Singh et al. further discloses wherein the monitored record includes a monitored acceleration time series of monitored acceleration values over the given time period (the record includes a monitored acceleration time series of monitored acceleration values over the given time period; paragraph [0087]), the given time period including a plurality of identical and consecutive sub-periods (the acceleration is monitored between the hours of 1 AM and 2AM to obtain a historical acceleration metric; paragraphs [0087], [0193]), and each monitored acceleration value of the monitored acceleration values being indicative of an acceleration of the monitored animal over a respective sub-period of the sub-periods (the monitored acceleration metric is indicative of the acceleration of the animal over the time period; paragraphs [0087]. [0193], [0194]), wherein the processing circuitry is further configured to: determine the monitored acceleration values in the monitored acceleration time-series that are less than or equal to an acceleration threshold (temperature readings above a certain temperature and movement readings below a certain level may be associated with an infected animal; paragraphs [0066], [0087]); and wherein the predict is also based on a determination that at least a predefined percentage of the monitored acceleration values are less than or equal to the acceleration threshold (movement readings below a certain level are associated with an infected animal, the average time the head tilt is above/below a particular threshold may be used to determine the health state of the animal; paragraphs [0066], [0125]).
As per claim 14 and parallel claim 29, Singh et al. further discloses wherein the processing circuitry is further configured to: provide historical acceleration values for one or more animals (historical acceleration values of the animals; paragraph [0087]), each historical acceleration value of the historical acceleration values being indicative of the acceleration of a respective animal of the one or more animals over a second respective sub-period (the historical acceleration values being indicative of the acceleration of a respective animal of the one or more animals over a period of 1AM-2AM; paragraphs [0087], [0193]), being earlier than and of an identical duration to the respective sub-period (the historical acceleration values will inherently be earlier than the respective sub-period; paragraph [0087]); and determine the acceleration threshold, based on the historical acceleration values (the acceleration threshold is based on the historical acceleration values; paragraphs [0087], [0193]).
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) 4, 5, 9, 19, 20, 24, and 25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Singh et al. as applied to claims 1 and 16 above, further in view of Webster et al. (Previously Cited).
Singh et al. teach a system and method for predicting illness, death, or abnormal conditions of an animal, including all of the claim features except providing details of the temperature range of the collected temperature measurements, or use of particular thresholding analysis as part of the predictions.
As discussed in the Non-final rejection, Webster et al. teaches an alternate system and method for automated monitoring of ruminant health and breeding, which relies upon measurement of temperature (or other vital sign) for, among other things, analysis/prediction of health results of the monitored animal.
Webster et al. recognizes for each measurement cycle, a temperature difference between a peak temperature value of the monitored temperature values in the respective cycle and a valley temperature value of the monitored temperature values in the respective cycle is greater than or equal to a predetermined difference (the system detects diurnal temperature cycles, which includes difference between a daily maximum temperature and a daily minimum temperature of the animal, and comparing the animal data record of read temperature to a baseline diurnal value and, given that difference, signal disease when that difference falls outside a threshold parameter; paragraphs [0126), [0134], [0139]). Webster et al. further discloses wherein the predetermined difference is at least 4 °C (the system detects diurnal temperature cycles, which includes the difference between a maximum temperature and a minimum daily temperature of the animal, and comparing the animal data record of read temperature to a baseline diurnal value and, given that difference, signal disease when that difference falls outside a threshold parameter, and so is capable of having a difference of a least 4 °C; paragraphs [0126], [0134], [0139]).
Additionally, Webster et al. discloses wherein the processing circuitry is further configured to: determine, for each cycle of the cycles, whether the peak temperature value for the respective cycle is greater than or equal to a temperature threshold (after a baseline is established, the default setting may be revised to a high alert temperature of 105.5° F. or 40.8° C; paragraph [0090]); wherein the predict is indicative of a number of the cycles for which the peak temperature value is greater than or equal to the temperature threshold being greater than or equal to a predefined number (an alert is only created once a specific percentage of temperature points are outside of the set temperature parameters over a specific period of time; paragraphs [0055], [0084], [0097]).
It would have been within the skill level of the art to modify Singh et al. to rely on known aspects of temperature variations in monitored cows, as discussed in Webster et al., including to analyze data having difference of at least 4 ⁰C and to incorporate temperature related thresholds to the predication analysis, as taught by Webster et al., since it has generally been held to be within the skill level of the art to rely on known properties of a measured system when determining a corresponding operating range and to provide additional criteria for improving an analysis result.
Response to Arguments
Applicant's arguments filed April 27, 2026 have been fully considered but they are not persuasive.
Applicant remarks that the amendments to the independent claims incorporate details from originally filed claim 2 sufficient to overcome the rejections under 35 USC 101. As noted in the rejection above, Applicant has amended the claims to incorporate some, but not all, of the limitations of original claim 2. As such, the claim scope of the independent claims is broader than what was presented in original claim 2. The rejection is not overcome, as discussed in the updated statement of the rejection.
With regard to the prior art rejections under 35 USC 102(a)(1), particularly regarding Singh, Applicant in summarizing the teaching of Singh, recognizes that the prior art includes use of machine learning/ML processing/techniques as part of the invention, yet alleges that the claimed use of ML differentiates from that of Singh.
This is not found to be persuasive, as the amended claim language is broad in the requirements regarding ML, such that one of ordinary skill in the art would readily understand that the use of ML in Singh (even consistent with Applicant's summary of the teaching) is within the metes and bounds imposed by the claim text.
Although Applicant argues that in the amended claim "the monitored temperature time series is itself analyzed using the ML model" as a way of distinguishing from the manner in which Singh implements their machine learning processing, the claim language itself imposes no such requirement on the relationship between the monitored time series data, any potential modification thereof, and the ML model. Without claim language limiting the scope in the alleged manner, differences found in the corresponding disclosures cannot serve to show that the claimed invention is separate from the prior art. To reiterate: the amended language directed to analyze "using a machine learning (ML) model" is met as long as ML is used for any aspect of the analysis, and can be equally applied to measurement/raw values or pre-processed values.
Applicant’s arguments, see Remarks, filed April 27, 2026, with respect to the rejection(s) under 35 USC 112(b) have been considered and are persuasive. The rejections have been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ERIC FRANK WINAKUR whose telephone number is (571)272-4736. The examiner can normally be reached Mon-Fri 9 am - 6 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, Chuck Marmor, II can be reached at 571-272-4730. 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.
/ERIC F WINAKUR/Primary Examiner, Art Unit 3791
Prosecution Insights