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
The following is a Non-Final, first Office Action responsive to Applicant’s communication of 4/25/23, and claims of 8/29/23, Claims 1-11 are pending in the instant application and have been rejected below.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Korea on10/26/2020. It is noted, however, that applicant has not filed a certified copy of the KR10-2020-0139309 application as required by 37 CFR 1.55. Examiner notes that on 6/2/25, a notice that a certified copy of the foreign application 10-2020-0139309 was failed to be retrieved.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/25/23 is being considered by the examiner.
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 3, 6, 8-10 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.
Regarding claims 3, 6, 8-10, the phrase "may" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 3 - the processing step (S20) and the adjusting step (S30) may be repeatedly performed at least once.
Claim 6 - wherein the processing step (S20) may be performed based on machine learning.
Claim 8 - wherein at least one of usage amount of individuals, production amount of individuals, death amount of individuals and inventory amount of individuals may be classified and recognized according to at least one of the gender of individuals, color of individuals, age of individuals, and management state of individuals.
Claim 9 - wherein MD (Mating Delay) may be an additionally delayed time (week) assuming that the mean is conception one week after the start of mating.
Claim 10 - MD (Mating Delay) may be an additionally delayed time (week).
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without reciting significantly more.
Step One - First, pursuant to step 1 in MPEP 2106.03, the claim 1 is directed to a method which is a statutory category.
Step 2A, Prong One - MPEP 2106.04 - The claim 1 recites–
An experimental animal managing method comprising:
a receiving step (S10) of receiving user log data and individual data of an experimental animal; and
a processing step (S20) of calculating at least one of
an expected production amount of individuals, the expected number of cages to be produced, and the expected total number of cages of the experimental animal on the basis of the user log data and the individual data, and
optimizing at least one among the calculated expected production amount of individuals, the calculated expected number of cages to be produced, and the calculated expected total number of cages according to optimization requirement.
As drafted, this is, under its broadest reasonable interpretation, within the Abstract idea grouping of “certain methods of organizing human activity” (Commercial or legal interactions - marketing activities or behaviors) and mathematical relationships, as here receive data and then calculate either expected production amount, expected number of cages to be produced, or expected total number of cages, then optimizing one of the values calculated based on “optimization requirement”. An example of “optimization requirement” is in claim 4 of considering difference between demand and supply of the experimental animal, which is another mathematical relationship as well as a marketing activity (market demand planning). Accordingly, claim 1 is directed to an abstract idea for marketing and expected cages optimized to consider the supply and demand/needs from historical/”log” information.
Step 2A, Prong Two - MPEP 2106.04 - This judicial exception is not integrated into a practical application. At this time, there are no additional elements such as a computer even recited. Examiner suggests as initial step, that Applicant attempt to put a computer in the claim to perform the calculations, but it is unclear where explicit support may be ([0075] as published has a database; 0077 has processing device). Even once a general recitation of computer/processing device is added, the claim 1 additional elements of “computer/processing device” that is performing a “processing step” amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). Accordingly, the 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 claim also fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, and/or an additional element applies or uses 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 more than a drafting effort designed to monopolize the exception. See 84 Fed. Reg. 55.
Step 2B in MPEP 2106.05 - The claim does 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 of a processing device (even if amended in the future), is considered MPEP 2106.05(f) (Mere Instructions to Apply an Exception – “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Even if a computer is added, “receiving” data in the 1st step is considered a conventional computer function. See MPEP 2106.05(d)(II) Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362.
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. The claim is not patent eligible. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Claim 2 further narrows the abstract idea by adjusting the numerical values.
Claim 3 further narrows the abstract idea by adjusting and repeating calculations.
Claim 4 further narrows the abstract idea by stating the “optimization requirement” is considering in the direction of minimizing difference between demand and supply of the experimental animal, which is another mathematical relationship as well as a marketing activity (market demand planning).
Claim 5 further narrows the abstract idea by stating the “optimization requirement” is considering in the direction of maximizing production or variation of individual [animals], which is another mathematical relationship as well as a marketing activity (market demand planning).
For claim 6, Examiner notes “machine learning,” is not required at this time as it recites “processing step (S20) may be performed based on machine learning.” Even if claim 6 is amended to positively recite “machine learning,” this is just considered “apply it [abstract idea] on a computer MPEP 2106.05(f)) and individually or in combination is consideration “field of use” (MPEP 2106.05h).
Claims 7-8 narrow the abstract idea by reciting either usage amount, production amount, death amount or inventory amount may be classified by either gender, color, age, or management state. First, these limitations are descriptive and are not functionally involved in the calculation, and not entitled to patentable weight. See MPEP 2111.05 “where the claim as a whole is directed to conveying a message or meaning to a human reader independent of the intended computer system, and/or the computer-readable medium merely serves as a support for information or data, no functional relationship exists.” To extent there is a function of classifying/grouping some of the data from claim 1, this is considered organizing the market planning data of claim 1, and mental evaluation abstract idea grouping.
Claims 9-11 include explicit mathematical relationships and mathematical calculations, further narrowing the abstract idea.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
For more information on 101 rejections, see MPEP 2106.
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Festing et al., "A method for calculating the area of breeding and growing accommodation required for a given output of small laboratory animals," 1968, Laboratory Animals Vol. 2.2, pages 121-130 in view of Ralston (US 2019/0012497).
Concerning claim 1, Festing describes:
An experimental animal managing method (Festing page 121, Abstract – method for calculation of area of animal accommodation needed to produce a given output of small laboratory animals; methods depends on determining the number of cages needed) comprising:
a receiving step (S10) of receiving user log data and individual data of an experimental animal (Festing – see page 122, “Number of cages” section – OW = average number of animals to be used per week; D = “number of growing future experimental animals maintained per breeding-size cage); and
a processing step (S20) of calculating at least one of
an expected production amount of individuals, the expected number of cages to be produced, and the expected total number of cages of the experimental animal on the basis of the user log data and the individual data (Festing – see page 122, “Number of cages” section – P = productivity – the number of weaned animals produced per breeding cage per week; see page 122 – “Number of cages” section - deducing number of cages needed (N) based on data of OW (average number to be used per week), P (productivity – weaned animals produced per breeding cage), K (proportion of animals not suitable), W (maximum length of growing period in weeks, D (number of growing future experimental animals maintained per breeding-size cage).
To any extent “processing” is referring to a computer, such as Applicant’s [0076] as published stating “processing device,” Ralston discloses:
a “processing” step (S20) (Ralston see par 22 - he system controller 122 in an embodiment includes at least one processor and at least one memory to store operating instructions implemented by the at least one processor) of calculating at least one of
an expected production amount of individuals, the expected number of cages to be produced, and the expected total number of cages of the experimental animal on the basis of the user log data and the individual data (Ralston –see par 45 - Based on historical trends across the entire system embodiments may use prediction strategies such as machine-learning to forecast key events for animals and/or their environment. Prediction can help a farmer/owner make decisions about the welfare of all animals under their control and to have context about expected behavior compared to actual behavior for new animals they have never raised before.)
Festing and Ralston disclose:
optimizing at least one among the calculated expected production amount of individuals, the calculated expected number of cages to be produced, and the calculated expected total number of cages according to optimization requirement (Festing – “optimization requirement” interpreted based on dependent claim 4 - see page 124, last paragraph – page 125, 1st paragraph – strains of mice as source of breeding nuclei; strains of CBA and BALB/c; see page 126, 2nd to last paragraph - Inspection of Table 2 shows that in both cases the estimated demand was in reasonable agreement with actual demand. Also, the value of F (the proportion of animals not used due to fluctuations in supply and demand) was successfully reduced from 50 to 15 per cent in the case of CBA, and from 34 to 13 per cent in the case of BALB/c. see page 127, 5th paragraph - Having obtained an estimate of future demand and, say, a standard error of this estimate, it is then relatively easy to calculate the area of accommodation needed to have a 90 (say) and a 95 per cent probability that there will be sufficient space to meet demand)
Ralston see par 45 - An example of prediction is upward or downward trend of egg or milk production which can help people make decisions about when to raise more or less animals in order to achieve the desired outcome. The system can take a desired outcome and help the individual manage their animals in order to achieve that objective. Although the system is described herein with particular application to chicken coops, it is to be understood that the system is not limited to such and may be utilized with various types of animals, livestock, and general animal husbandry).
Both Festing and Ralston are analogous art as they are directed to planning and calculating future decisions for animals (Festing Abstract; Ralston Abstract, par 45). Festing discloses number of cages needed for a given output for a week. Ralston improves upon Festing by disclosing using a computer to execute instructions and using machine learning for predictions for animals to achieve a desired outcome (See par 22, 45). One of ordinary skill in the art would be motivated to further include using a computer to execute instructions and using machine learning for predictions for animals to achieve a desired outcome to efficiently improve upon the calculation for number of cages needed in Festing.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the determining of the number of cages needed for laboratory animals in Festing to further use a computer and machine learning for predictions related to animals as disclosed in Ralston, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 2, Festing and Ralston disclose:
The experimental animal managing method according to claim 1, comprising an adjusting step (S30) of adjusting at least one of the number of individuals and the number of cages of the experimental animal based on at least one of the expected production amount of individuals, the expected number of cages to be produced, and the expected total number of cages optimized in the processing step (S20) (Festing – see page 122 - “Number of cages” section - D (number of growing future experimental animals maintained per breeding-size cage – this figure can be adjusted if in fact a different sort of cage is used for growing only; resulting in a changed N (Number of cages needed))
Obvious to combine Festing and Ralston for the same reasons as claim 1 above.
Concerning claim 3, Festing and Ralston disclose:
The experimental animal managing method according to claim 2, wherein the receiving step (S10), the processing step (S20) and the adjusting step (S30) may be repeatedly performed at least once (Festing – see page 122 - “Number of cages” section - D (number of growing future experimental animals maintained per breeding-size cage – this figure can be adjusted if in fact a different sort of cage is used for growing only; resulting in a changed N (Number of cages needed); see page 123, last section - The value of many of the parameters will be determined by the type of animal to be produced, the equipment available, and the type of demand to be catered for. Figures for productivity can be obtained from previous records, and an informed guess made about future trends; see page 126, 1st section – OW (average number of animals to be used per week ) determined by … demand estimated as average demand over the previous year;
see also Ralston – see par 18 - The at least one system controller 60 stores the sensor data in a database 64 and uses the stored sensor data to determine trends, anomalies and factors that influence animal behavior. see par 40 - By trending the data averages within the system it will be possible to see how animals behaved compared to their peers in other locations/flocks or themselves from another period of time and/or under different circumstances. Trending this data allows for comparisons that over time that can showcase abnormalities which may deserve additional attention in order to secure the animal's welfare or increase production.)
Obvious to combine Festing and Ralston for the same reasons as claim 1 above.
Concerning claim 4, Festing and Ralston disclose:
The experimental animal managing method according to claim 1, wherein the optimization requirement is processed in the direction of minimizing at least one of the difference value between the individual demand and the individual supply of the experimental animal, the expected inventory amount of individual and the total number of cages (Festing – see page 124, last paragraph – page 125, 1st paragraph – strains of mice as source of breeding nuclei; strains of CBA and BALB/c; see page 126, 2nd to last paragraph - Inspection of Table 2 shows that in both cases the estimated demand was in reasonable agreement with actual demand. Also, the value of F (the proportion of animals not used due to fluctuations in supply and demand) was successfully reduced from 50 to 15 per cent in the case of CBA, and from 34 to 13 per cent in the case of BALB/c. see page 127, 5th paragraph - Having obtained an estimate of future demand and, say, a standard error of this estimate, it is then relatively easy to calculate the area of accommodation needed to have a 90 (say) and a 95 per cent probability that there will be sufficient space to meet demand).
Concerning claim 5, Festing and Ralston disclose:
The experimental animal managing method according to claim 4, wherein the optimization requirement is processed in the direction of maximizing at least one of the production amount of individuals and the variation amount of individuals of the experimental animal (Festing – see page 126, 1st section – culling sterile pairs and slow breeders considerably increases the apparent productivity; Sere page 128, 2nd paragraph - Production varied from 53 to 139 mice in a four-week period, and the shelf life of the mice was relatively long since they were available for use from about four weeks to about twelve weeks of age).
Concerning claim 6, Festing and Ralston disclose:
The experimental animal managing method according to claim 1, wherein the processing step (S20) may be performed based on machine learning (Ralston –see par 45 - Based on historical trends across the entire system embodiments may use prediction strategies such as machine-learning to forecast key events for animals and/or their environment. Prediction can help a farmer/owner make decisions about the welfare of all animals under their control and to have context about expected behavior compared to actual behavior for new animals they have never raised before).
Obvious to combine Festing and Ralston for the same reasons as claim 1 above.
Concerning claim 7, Festing and Ralston disclose:
The experimental animal managing method according to claim 1, wherein the individual data comprises at least one of usage amount of individuals, production amount of individuals (Festing see page 122, “Number of cages” section – P = productivity – the number of weaned animals produced per breeding cage per week; see page 122, “Number of cages” section – OW = average number of animals to be used per week; see page 126, 1st section – OW (average number of animals to be used per week ) – demand estimated as average demand over previous year), death amount of individuals, inventory amount of individuals and number of storage spaces of individuals (Festing page 122, last paragraph- page 123, 1st paragraph – formula 2 for calculating area of animal room needed; R = number of units of floor space; Q = width of a single cage; T = number of tiers (shelves) of cages for the given species).
Concerning claim 8, Festing and Ralston disclose:
The experimental animal managing method according to claim 7, wherein at least one of usage amount of individuals, production amount of individuals, death amount of individuals and inventory amount of individuals
may be classified and recognized according to at least one of the gender of individuals, color of individuals, age of individuals, and management state of individuals (Festing page 122, “Number of cages” section – K (proportion of animals not suitable – e.g. surplus females, if males are mostly used for research; see page 125, 2nd paragraph and Table 2 – Information is given of the number of breeding females, average sales per week, average number cuIled and average productivity;
See Ralston par 29 - By gathering data about the behavior of the animal and combining it with environmental information various issues associated with the animal can be identified and addressed. For example, if a female chicken is sitting on their nest for approximately 45 minutes the bird is likely laying an egg. If the bird is on the nest for greater than approximately 90 minutes, the bird may be broody. If either of these conditions take place during periods of low light, the bird may be not allowed on the roost which indicates a low position in the bird's hierarchy. The observed behaviors are gathered across flocks along with additional data such as breed, age, gender, location, temperature, weather data etc. The combination of this data allows further intelligence to be gathered and derived about the specific circumstances which will be optimal for each animal/group of animals.).
Obvious to combine Festing and Ralston for the same reasons as claim 1 above.
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Festing et al., "A method for calculating the area of breeding and growing accommodation required for a given output of small laboratory animals," 1968, Laboratory Animals Vol. 2.2, pages 121-130 in view of Ralston (US 2019/0012497), as applied to claims 1-8 above, and further in view of Ishiwaka et al., "Characteristics of pregnancy following mating in three types of estrus in the captive harvest mouse (Micromys minutus)," 2019, Mammal Study, Vol. 44, No. 4, pages 253-259.
Concerning claim 9, Festing and Ralston disclose:
The experimental animal managing method according to claim 1, wherein the expected production amount of individuals is calculated according to
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Festing page 122 equation – N (number of cages needed) = OW average number of animals to be used per week multiplied by (1/ (P productivity for number of weaned animals produced per breeding cage per week) * (1- (K proportion not suitable for use) + W length of growing period / D number of future animals per breeding-size cage
wherein DD (Double Delivery) is the pregnancy success rate (0<DD<1) in the postpartum estrous period (Ishiwaka page 253, col. 1, 2nd paragraph - With regard to the delivery interval, it is important to understand when and how estrus leading to pregnancy occurs. Estrus in nonpregnant and nonlactating females is known as cyclic estrus (CE).; Females of many rodent species have another type of estrus, postpartum estrus (PPE), in which females become receptive and can mate
shortly after giving birth. page 253, col. 2, 1st paragraph - Another type of estrus occurs during the lactation period after PPE ceases (which we abbreviate as LEAP) in some rodent species. page 255, Results “Copulation and pregnancy also occurred in all six trials of mating in PPE (Table 2). The introduction of a male during the lactation period when PPE had ceased resulted in estrus (LEAP), copulation, and pregnancy of all seven trials.” (Table on page 256); page 257, Col. 1, Discussion - Our definition of an occurrence of copulation with ejaculation might be correct because of the very high success rate of pregnancy in all the mating trials. Although copulation was observed once in each mating trial in PPE and several times at intervals for 2–3 h in the mating trials in the other two types of estrus, we used the time when copulation was first confirmed by the video during each trial for calculating the length of the gestation period and the time from the introduction of the male to copulation. ), wherein R is the cycle from birth to next birth in the reproduction cycle (Ishiwaka – see Table 3 – Time from previous parturition to copulation (days) – average 6.29 days; Gestation length average 17.75 days), wherein MD (Mating Delay) may be an additionally delayed time (week) assuming that the mean is conception one week after the start of mating ( see page 258, Col. 1, 2nd paragraph - The prolonged period of gestation due to delayed implantation is considered to occur to avoid excessive demands on the mother from the fetuses and suckling pups (Norris and Adams 1981; Johnson et al. 2001; Rutkowska et al. 2011).
Festing equation),
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Modifying Festing with values from Ishiwaka results in Table 2 (PPE) or table 3 (LEAP), and then using the “known technique” in Ishiwaka of delaying to avoid excessive demands on the mother, it will increase the “W/D” portion of the question of “length of growing period in weeks”, increasing the number of cages).
Festing, Ralston, and Ishiwaka are analogous art as they are directed to planning future decisions for laboratory animals (Festing Abstract; Ralston Abstract, par 45; Ishiwaka Abstract, page 254, Col. 2; See page 257, Discussion – estimating time). Festing discloses number of cages needed for a given output for a week (page 122). Ralston discloses using a computer to execute instructions and using machine learning for predictions for animals to achieve a desired outcome (See par 22, 45). Ishiwaka improves upon Festing and Ralston by disclosing considering number of laboratory animals born relative to postpartum estrous periods and considering delaying next pregnancy to avoid excessive demands on a mother. One of ordinary skill in the art would be motivated to further include considering postpartum estrous periods for reproducing more animals and considering delaying next pregnancy for reproducing laboratory rodents to efficiently improve upon the calculation for number of cages needed in Festing and the predictions in Ralston.
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the determining of the number of cages needed for laboratory animals in Festing to further use a computer and machine learning for predictions related to animals as disclosed in Ralston, to further consider periods of time relative to estrous cycles for reproducing mice/rodents and considering delaying as disclosed in Ishiwaka, since the claimed invention is merely a combination of old elements, and in combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable and there is a reasonable expectation of success.
Concerning claim 10, Festing, Ralston, and Ishiwaka disclose:
The experimental animal managing method according to claim 1, wherein the expected number of cages to be produced is calculated with Expected number of cages
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Festing page 122 equation – N (number of cages needed) = OW average number of animals to be used per week multiplied by (1/ (P productivity for number of weaned animals produced per breeding cage per week) * (1- (K proportion not suitable for use) + W length of growing period / D number of future animals per breeding-size cage
for Maverage – Festing discloses the number of cages discloses considering it – see page 122, 1st paragraph requires depends on “the numbers of growing animals per cage”; 2nd paragraph);
the DD (Double Delivery) is the pregnancy success rate (0<DD<1) in the postpartum estrous period (Ishiwaka page 253, col. 1, 2nd paragraph - With regard to the delivery interval, it is important to understand when and how estrus leading to pregnancy occurs. Estrus in nonpregnant and nonlactating females is known as cyclic estrus (CE).; Females of many rodent species have another type of estrus, postpartum estrus (PPE), in which females become receptive and can mate
shortly after giving birth. page 253, col. 2, 1st paragraph - Another type of estrus occurs during the lactation period after PPE ceases (which we abbreviate as LEAP) in some rodent species. page 255, Results “Copulation and pregnancy also occurred in all six trials of mating in PPE (Table 2). The introduction of a male during the lactation period when PPE had ceased resulted in estrus (LEAP), copulation, and pregnancy of all seven trials.” (Table on page 256); page 257, Col. 1, Discussion - Our definition of an occurrence of copulation with ejaculation might be correct because of the very high success rate of pregnancy in all the mating trials. Although copulation was observed once in each mating trial in PPE and several times at intervals for 2–3 h in the mating trials in the other two types of estrus, we used the time when copulation was first confirmed by the video during each trial for calculating the length of the gestation period and the time from the introduction of the male to copulation), wherein R is the cycle from birth to next birth after the reproduction cycle (Ishiwaka – see Table 3 – Time from previous parturition to copulation (days) – average 6.29 days; Gestation length average 17.75 days;
wherein, assuming that the mean is conception one week after the start of mating, MD (Mating Delay) may be an additionally delayed time (week) (Ishiwaka see page 258, Col. 1, 2nd paragraph - The prolonged period of gestation due to delayed implantation is considered to occur to avoid excessive demands on the mother from the fetuses and suckling pups (Norris and Adams 1981; Johnson et al. 2001; Rutkowska et al. 2011).
Festing equation)
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,.
Modifying Festing with values from Ishiwaka results in Table 2 (PPE) or table 3 (LEAP), and then using the “known technique” in Ishiwaka of delaying to avoid excessive demands on the mother, it will increase the “W/D” portion of the question of “length of growing period in weeks”, increasing the number of cages).
Obvious to combine Festing and Ralston and Ishiwaka for the same reasons as claim 9 above.
Concerning claim 11, Festing, Ralston, and Ishiwaka disclose:
The experimental animal managing method according to claim 1, wherein the expected total number of cages is calculated according to Expected total number of cages
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Festing see page 127, 3rd paragraph - In this case, it may even be possible
to assign a confidence interval to the estimate of demand. Where market
research is not practicable or economic, a more subjective estimate will have
to be used, but again it may be possible to postulate a range of possible
demand. page 127, 5th paragraph - Having obtained an estimate of future demand and, say, a standard error of this estimate, it is then relatively easy to calculate the area of accommodation needed to have a 90 (say) and a 95 per cent probability that there will be sufficient space to meet demand; The cost of extra space can then be balanced against the cost of failing to meet demand, and the probability that this will occur. Suppose, for example, that demand is estimated to be 2000 ± 200 animals per week (disclosing calculating a minimum number of cages and a maximum number of cages)
wherein T.sub.s is the minimum age of use of experimental animals, wherein T.sub.t is the maximum age of use of the experimental animal (Festing – see page 122, 1st paragraph – age and type of animal to be produced; see page 128, 2nd paragraph - shelf life of the mice was relatively long since they were available for use from about four weeks to about twelve weeks of age; (disclosing Ts, Tt (range of ages of animals));
see also Ralston – see par 39 - Combining factual information about each animal with the data gathered by the sensor-network provides the opportunity to develop deep intelligence about individual animals and the flock as a whole as well. This information can further be aggregated to further understand behavior across flocks, regions, breeds, age, social status etc.), wherein T.sub.w is the age at which the experimental animal is weaned (weaning: separated from the mother) (Festing - see page 125, last paragraph – mice weaned at 3 weeks of age (disclosing Tw (age of weaning)); Ishiwaka – see page 257, Table 2-3 – Gestation length, time from the introduction of the male to copulation, and number of pups at weaning in mating of harvest mouse females in estrus occurring in the later part of the lactation period after postpartum estrus (LEAP); ), wherein K is a correction constant, wherein C.sub.m is the number of mating cages of experimental animals (Festing page 127, last paragraph - If the productivity is half what was estimated, then double the number of breeding cages will be required, but the number of growing cages will stay constant.), wherein L.sub.avg is the average number of litters (n) per birth (Festing page 124, Table 1 – P value – e.g. “good non-inbred pairs, trios – 2.0-4.0), wherein M.sub.avg is the average number of experimental animals (n) per cage (Festing – see page 122, 1st paragraph - The method used to calculate the area of accommodation required depends on determining, first, the number of cages required (which depends on the number, age and type of animal to be produced, the productivity, the size of the cage, and the numbers of growing animals per cage),), wherein R is the cycle from birth to next birth after the reproduction cycle (Festing page 122, “Number of cages” section – W = maximum length of the growing period in weeks), wherein B.sub.s is the age of the experimental animal at the start of breeding, wherein B.sub.t is the age of the experimental animal at the end of breeding (Ishiwaka – see page 254, Col. 1, Animals – females of this species reported to have given birth at 62 days of age; females ranged from 60 to 210 days old (disclosing Bt and Bs for age of breeding animals); number of experienced deliveries of each individual at the mating is presented in Tables 1-3).
Obvious to combine Festing and Ralston and Ishiwaka for the same reasons as claim 9 above. In addition, Ralston and Ishiwaka improve upon Festing by considering age or breeding age of animals.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xiong (CN 103651248) – directed to simulating pig production (See Abstract)
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