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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/19/2025 has been entered.
Response to Arguments
Status of the claims
The Examiner acknowledges the applicant’s summary of the status of the claims, and the amendments to the claims.
Drawing Objection
The Examiner acknowledges and has fully considered the applicant’s arguments regarding the drawing objections. The Examiner acknowledges amendments to the drawings.
The applicant seemingly argues (Remarks page 6 paragraph 4 – page 7 paragraph 8) that the claimed limitations of a normal distribution, as well as sections with equiprobable (1/2N) partitioning, are shown in the amended drawings, figure 3. The Examiner respectfully agrees, however, the Examiner notes that the figure does not show exactly equiprobable partitioning (1/2N), it shows partitioning that is substantially equal to 1/2N, which is what is claimed in the amended claims. The Examiner withdraws the drawing objection regarding the normal distribution limitation as well as the equiprobable partitioning limitation.
The Examiner notes, however, that the applicant seemingly did not make amendments to the drawings nor further arguments regarding the limitations “wherein the single photon detector detects whether the dark count occurs when a trigger signal is applied”, nor the limitation regarding the plurality of section determined by a bit size set by a user, which was objected to in the Non-Final Office action (page 2) mailed on 05/20/2025, and which was not withdrawn by the Examiner in the Final-Rejection Office Action (page 3) mailed on 11/05/2025. For that reason, the objection to the drawings regarding these limitations remains.
35 U.S.C. 103
The Examiner acknowledges and has fully considered the arguments made by the applicant regarding the 35 U.S.C. 103 rejections.
Applicant argues (Remarks page 8 paragraph 8-9 – page 9 paragraph 1), that Jang (KR 101930271 B1), hereinafter, “Jang”, does not teach equiprobable normal distribution partitioning. The applicant argues that Jang teaches Poisson distribution. The Examiner respectfully notes that, while Jang teaches Poisson distribution, as pointed out in the Non-Final Rejection office action dated 05/20/2025, (Page 16, and in the footnote reference of the page, https://www.sciencedirect.com/topics/biochemistry-genetics-and-molecular-biology/poisson-distribution, 3.6.3.3 Poisson distribution), and the Final-Rejection office action dated 11/05/2025, (Page 7 and in the footnote reference of the page, Differences between the normal and Poisson distributions. The Analysis Factor RSS. (2017). https://web.archive.org/web/20170417122115/https://www.theanalysisfactor.com/differences-between-normal-and-poisson-distributions/#comments), Poisson distribution may resemble normal distribution. Furthermore, the applicant’s claimed distribution is based on number of dark counts (discrete value) vs probability, which Jang teaches dark counts vs probability. The Examiner further notes that with this sort of graph, with a discrete value x and continuous probability value y axis, with the described bell shaped curve, seemingly in the art is often called Poisson distribution rather than Normal distribution, see Han et al. (U.S. Patent Application Publication 20190278567 A1), hereinafter, “Han”, Fig. 12, and [0097] regarding a figure of dark noise distribution curve on a graph with dark noise (as dark count) vs probability, having the bell curve shape, and described as a Poisson distribution, see Wang et al, (F. -X. Wang et al., "Robust Quantum Random Number Generator Based on Avalanche Photodiodes," in Journal of Lightwave Technology, vol. 33, no. 15, pp. 3319-3326, 1 Aug.1, 2015, doi: 10.1109/JLT.2015.2432803), hereinafter, “Wang” section II regarding a description of a graph of number of detected photons vs probability described as a Poisson distribution, see Lin et al., (True random number generation based on arrival time and position of dark counts in a multichannel silicon photomultiplier, Jianming Lin, Yonggang Wang, Qiang Cao, Jie Kuang, Liwei Wang, Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China, Nov. 2019), hereinafter, “Lin” section II and Fig. 2 regarding a graph of dark count vs probability being a Poisson distribution graph, but having the bell curve shape resembling normal distribution.
The applicant further argues, (Remarks page 9 paragraph 2), that Lu’s bit size is not a motivation to combine. The applicant argues that Lu, (U.S. Patent Application Publication 2018/0046436 A1), hereinafter, “Lu” is silent regarding physical dark count or distribution mapping process. The applicant further argues that integrating Lu’s bit size in combination with Jang does not yield the claimed equiprobable normal distribution sectioning, and that the combination merely determines output length, not a statistical equalization or normal distribution partitioning. The Examiner respectfully points out that in the Examiner’s Non-final Rejection Office Action, dated 05/20/2025, page 20, and page 21, and in the Final Rejection Office Action, dated 11/05/2025, page 15, and page 18, Lu is brought in as a combination with Jang for the limitation of a bit size set by the user with the motivation of combination being that it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine the teachings of Jang with the bit size set by a user of LU because it allows the output to be the “desired number of random bits” [LU : ¶[0045]], Lu is not meant to be leaned on as teaching or suggesting dark count probability distribution, the Examiner used Jang for those limitations.
The applicant further argues, (Remarks page 9 paragraph 3), that Jang’s counter resets at M-bit intervals, inherently tied to hardware constraints, which the applicant argues renders Lu’s user set bit size parameter functionally irrelevant. The applicant further argues that one of ordinary skill would recognize no technical motivation to modify Jang’s counting process to incorporate Lu’s bit length control. The Examiner respectfully disagrees. The Examiner respectfully points out that Jang’s counter resets at M-bit intervals. Lu teaches that a user can set the bit size, meaning that M-bit could be set by the user. The applicant points out that Jang is hardware limited. The Examiner respectfully points out that even if the hardware has an upper bound limit, that does not mean that it is not possible or reasonable for a user to be able to set a bit size within the hardware constraints of the device. Furthermore, the Examiner respectfully points out that the motivation to combine Lu with Jang is because it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine the teachings of Jang with the bit size set by a user of LU because it allows the output to be the “desired number of random bits” [LU : ¶[0045]].
The applicant further argues, (Remarks page 9 paragraph 4-5) that Jang requires optional hash-based post-processing to correct bias for equal-probability outcomes, whereas the applicant’s invention does not, which result in unexpected improvement over the prior art. The Examiner respectfully disagrees. The Examiner respectfully points out that in the previous office actions, in the Non-Final Rejection dated 05/20/2025, and in the Final Rejection dated 11/05/2025, the optional hash-based post-processing of Jang is not relied upon for mapping the claims. As previously discussed in the Final Rejection dated 11/05/2025, page 4 paragraph 2 – page 5 paragraph 1, Jang, [0034] states, “All of the above methods 1, 2, and 3 have the best output distribution when M=1, and since the output probabilities of ‘0’ and ‘1’ ideally converge to 50%, there is no possibility of the probabilities being biased.” Jang [0034] further states that “Method 3 must capture the pulse counter value with a sufficiently large cycle (using a random number generation clock (RNG) generation clock) than the average generation period of the dark current pulse.” Furthermore, the Examiner notes that in Jang, [0018], states “In this way, the present invention implements a perfect random number generator by utilizing the quantum randomness of dark current, which was considered to be a target of minimization as a hindrance to photon measurement, which is the original purpose of SPAD, and although it is a quantum random number generator, unlike the existing quantum random number generator, it excludes the use of a light source and a semi-transparent mirror that inevitably has probability bias, and by enabling implementation on a single semiconductor chip, it provides random number characteristics with unbiased uniform randomness”. Furthermore, the Examiner notes that, regarding the hash-based post processing unit, paragraph [0039], states “in addition, the hash engine (602) is a block that implements a hash function in hardware that receives an H-bit input and generates an L-bit output, and is used for the purpose of removing bias that may exist in the output value of a random number generator.” Jang continues, paragraph [0042] which states, regarding the addition of the optional post processor “In addition, in the present invention, by applying a plurality of dark current generators (300) and a post-processor (600) as illustrated in Fig. 4, the random number generation bits can be expanded and the random number generation speed can be accelerated.” The Examiner further points out that Jang, [0034], as referenced above, describes uniform distribution of output values, and in particular points out that for the uniform distribution, method 3 must capture the pulse counter value with a sufficiently large cycle than the average generation period of the dark current pulse. As interpreted by the Examiner, this would mean that the clock is large enough to allow many dark counts to be recognized. From that, the Examiner respectfully points out to the figure, Fig. 4, with the hash based post processor in it. In Jang, Fig. 4, there are several of the dark count random number generators with their outputs being fed into the post processor. In Jang, [0042], as referenced above, Jang describes that applying a plurality of dark current generators to a post processor as in Fig. 4, the random number generation bits can be expanded and the random number generation speed can be accelerated. As interpreted by the Examiner, the (optional) hash-based post processor is used in combination with the plurality of dark current generators, whereas having several in combination produce a sufficient amount of counts within a period of time to allow for the uniform distribution rather than having to have a much longer clock cycle for enough to be produced as referenced in [0034], which is how it allows for random number generation speed to be accelerated. Furthermore, as interpreted by the Examiner, as referenced above, [0039], the (optional) hash-based post processor, removes bias which may exist. As interpreted by the Examiner, since the RNG produces uniform distribution when a sufficiently large clock is used, [0034], this would seemingly mean that bias may be introduced when a sufficiently large clock is not used to allow uniform distribution. From that, as referenced above, and as shown in Jang fig. 4, and referenced in Jang [0042], a plurality of RNGs can be combined to accelerate the output, which indicates a shorter clock used by each, but if they are combined, and put into the post processor, if they each may introduce bias through not having a sufficient number of counts individually due to accelerating and not having a sufficiently large clock, then the combination of all of the RNGs put together by the post processer may remove the bias due to the combination of all of the values from the RNGs. With that said, again, the Examiner does not rely on the post processor for the claim mapping, the Examiner only is demonstrating how Jang’s post processor seemingly may remove bias due to the option of not using a sufficiently large clock, whereas the Examiner maps the embodiment where a sufficiently large enough clock is used, which, as Jang seemingly describes in [0034] allows for no bias.
The applicant further argues, (Remarks table on pages 9-10), a summary of differences, presumably meant to be an indication of limitations not taught by the prior art. The Examiner respectfully disagrees. The applicant lists features of a difference of statistical model, normal distribution vs Poisson of Jang. The Examiner respectfully disagrees for at least the reasons listed above. The applicant further lists the features of section division, stating that Jang is not for multi-bit. The Examiner respectfully disagrees, Jang uses an M-bit counter, and gives an example of using M equal to 1 in [0034], but in [0030] discusses M being an integer greater than or equal to 1. The applicant further lists the feature of user control, with the applicant’s claimed invention having a bit size which sets the number of sections. The Examiner respectfully disagrees for at least the reasons referenced above regarding Lu teaching bit size set by user and to Jang describing an example of two sections determined by bit size in [0034]. The applicant further lists the feature of equal probability across sections, with a comment regarding that Jang teaches that bias remains. The Examiner respectfully disagrees for at least the reasons referenced above regarding Jang teaching a sufficient enough clock to ensure equal probability, and regarding the references used to show that Poisson distribution resembles Normal distribution with a sufficient enough data sampling as well as the references used to show similarly described graphs of number of counts vs probability described as Poisson distribution. The applicant further lists the feature of need for post processing and comments that Jang suggests use of hash-based post processing. The Examiner respectfully disagrees for at least the reasons referenced above regarding the hash-based post processor being optional, and that Jang only teaches that the post processor removes bias which may exist, and that the Examiner does not rely on the post processor embodiment for mapping. The applicant further lists that Jang has a result of a biased Poisson count distribution. The Examiner respectfully disagrees for at least the reasons referenced above regarding Jang teaching a sufficiently large enough clock to remove bias, Jang suggesting that dark counts remove bias over prior art, and regarding the references/reasonings above regarding Jang’s Poisson distribution.
The applicant further argues, (Remarks page 10 paragraph 1) that Jang nor Lu taken individually or in combination, renders the amended claims 1 and 8 obvious. The Examiner respectfully disagrees for at least the reasonings referenced above.
The applicant further argues, (Remarks page 10 paragraph 2) that dependent claims recited additional features not taught or suggested by Jang or Lu, such as the trigger signal detection, normal distribution following. The Examiner respectfully disagrees regarding the normal distribution following for at least the reasons referenced above. The Examiner further respectfully disagrees regarding the trigger signal detection for at least the reasons listed in the Non-final Rejection Office Action dated 05/20/2025, (page 15 paragraph 2), as well as the reasons listed in the Final Rejection Office Action dated 11/05/2025, (page 15 paragraph 2).
The applicant further argues, (Remarks page 11 paragraph 1), that the drawing objections are overcome and the 103 rejections have been overcome due to Jang nor Lu teaching or suggesting the amended limitations. The Examiner respectfully agrees that the amended drawings overcome the objection regarding a graph with normal distribution with partitions substantially equal to 1/2N, but respectfully disagrees that the amended drawings overcome all objections, as referenced above in the section regarding drawing objections. Furthermore, the Examiner respectfully disagrees that the amended claims overcome the 103 rejections, for at least the reasons listed above.
Conclusion
The Examiner acknowledges the applicant’s conclusion statements.
Claim Objections
Claims 1, and 8 are objected to because of the following informalities:
Claim 1 appears to contain a grammatical error and should be change to: “recognizing the number of detection times ”.
Claim 8 appears to contain a grammatical error and should be changed to: “recognize the number of detection times ”.
Appropriate correction is required.
Drawings
The drawings are objected to under 37 CFR 1.83(a). The drawings must show every feature of the invention specified in the claims. Therefore, the “wherein the single photon detector detects whether the dark count occurs when a trigger signal is applied”, and “wherein the number of sections (2N) is determined based on a bit size N set by a user” must be shown or the feature(s) canceled from the claim(s). As stated in the Final-Rejection Office Action dated 11/05/2025, (page 3), a trigger signal is typically an instantaneous signal that starts/stops something in a circuit from occurring. The applicant, however, seemingly has the trigger signal be a time period in which dark counts are counted rather than an instantaneous signal, “wherein the single photon detector detects whether the dark count occurs when a trigger signal is applied”. The Examiner suggests a timing diagram to showcase the claimed feature of “the single photon detector detects whether the dark count occurs when a trigger signal is applied”. No new matter should be entered.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 4, 8, 10, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Jang in view of LU.
With regards to claim 1, Jang teaches:
A method for generating a random number, (¶[0001] regarding a random number generator using dark current (as dark count));
which is performed by a computing device including at least one processor, (Fig. 6 regarding method 3 M-bit Counter (as a processor); ¶[0033] regarding an M-bit pulse counter (as a processor) incrementing the counter value every dark current pulse);
the method comprising: recognizing the number of detection times at which a single photon detector (SPD) detects a dark count; (Fig. 3 items 100 and Dark Current Pulse (as a dark count); ¶[0001]regarding using a single photon avalanche diode (as a single photon detector) for generating a dark current pulse (as a dark count) for random number generation; ¶[0033] regarding an M-bit pulse counter incrementing the counter value every dark current pulse);
and generating a random number based on a bit value allocated to a section including the number of detection times among a plurality of section, (Fig. 3 items 300, Dark Current Pulse, 400, and M-bits digital signal; ¶[0001] regarding a random number generator using dark current (as dark count); ¶[0030] regarding the pulse-random number converter (Fig. 3 item 400) generating M-bit digital random numbers using method 3, the number of dark current pulses (as dark counts generated within a certain time period;¶[0033] regarding method 3 incrementing a counter for each dark current pulse with the counter output value following Poisson distribution with the dark pulse (as a dark count) frequency for a given period as a parameter; ¶[0034 regarding method 3 having a sufficiently larger clock cycle than the dark current pulse generation period, and an example output probability falling within generating a '0' or '1' (as a plurality of sections); As interpreted by the Examiner, [0033]-[0034] describes method 3 increasing a count whenever a dark count comes in. Furthermore it states that the output, as in the output random numbers, follow a Poisson distribution. [0034] describes an example of a 1-bit value output in the Poisson distribution with an even probability of '0' or '1' output. As interpreted by the Examiner, what is being described is two sections in a Poisson distribution, with outputs '0' and '1' assigned as separate "sections" for which the number of dark counts to fall in);
wherein the section refers to a group of values and the sections refer to groupings of values, (¶[0030] regarding the pulse-random number converter (Fig. 3 item 400) generating M-bit digital random numbers using method 3, the number of dark current pulses (as dark counts) generated within a certain time period;¶[0033] regarding method 3 incrementing a counter for each dark current pulse with the counter output value following Poisson distribution with the dark pulse (as a dark count) frequency for a given period as a parameter; ¶[0034] regarding method 3 having a sufficiently larger clock cycle than the dark current pulse generation period, and an example output probability falling within generating a '0' or '1' (as a plurality of sections). As interpreted by the Examiner, [0030] describes counting a plurality of dark current pulses generated in a certain time period, and [0034] describes an example output of '0' or '1' based on the number of dark counts (as a group of values for a section outputting '0' and a group of values for a section outputting '1'));
wherein each of the plurality of sections corresponds to an equiprobable partition of a normal distribution of dark count detection values such that a probability that the dark count is detected in each section is substantially equal to 1/2N, (¶[0034] regarding an example of a bit size of 1 and an example output probability falling within generating a '0' (as a first section) or '1' (as a second section) with the probability for each section being 50% As interpreted by the Examiner, having a bit size of 1 indicates the need for two sections (in this example, one section for ‘0’, and another for ’1’). Furthermore, as interpreted by the Examiner, having a bit size of 1 indicates that the probability for each section would need to be 1/21 (or 50%) according to 1/2N);
and wherein the number of sections (2N) is determined based on a bit size N. (¶[0034] regarding an example of a bit size of 1 and an example output probability falling within generating a '0' (as a first section) or '1' (as a second section) with the probability for each section being 50%; As interpreted by the Examiner, having a bit size of 1 indicates the need for two sections (in this example, one section for ‘0’, and another for ’1’) which falls in line with 2N.);
Jang does not explicitly teach:
bit size set by a user
However, Lu teaches:
bit size set by a user (¶[0045] regarding the user specifying the number of bits generated by a random number generator)
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine the teachings of Jang with the bit size set by a user of LU because it allows the output to be the “desired number of random bits” [LU : ¶[0045]].
With regards to claim 3, Jang in view of Lu teaches the method of claim 1, as referenced above.
Jang further teaches:
wherein the single photon detector detects whether the dark count occurs when a trigger signal is applied, by the at least one processor, at a predetermined time interval. (Fig. 6 regarding the RNG generation clock (the low logic value being considered the trigger signal); Fig. 7 regarding method 3’s dark current pulse (as dark count), and RNG generation clock (the low logic value being considered the trigger signal); ¶[0001] regarding the single photon avalanche diode (as a single photon detector) detects dark current and generates dark current pulses (as dark counts generated within a predetermined time interval; Fig. 6 regarding method 3 M-bit Counter (as a processor); ¶[0033] regarding an M-bit pulse counter (as a processor) incrementing the counter value every dark current pulse).
With regards to claim 4, Jang in view of Lu teaches the method of claim 1, as referenced above.
Jang further teaches:
wherein a probability value that the dark count will be detected by each real value included in each of the plurality of sections follows a normal distribution. (¶[0033] regarding method 3 of the random number generator generating random values based on dark current pulses (as dark counts) within a given time period. Furthermore, regarding the average dark pulse (as dark count) frequency in a given time period following a Poisson distribution; ¶[0034] regarding method 3 having a sufficiently larger clock cycle than the dark current pulse generation period, and an example output probability falling within generating a '0' (as a first section) or '1' (as a second section) with the probability for each section being 50% so there is no possibility that the probability is concentrated; As noted by the examiner, normal distribution is used for parameters that have a continuous value, whereas counting dark counts are of discrete values. Furthermore, Poisson distribution, with a large enough sampling size, 1resembles a normal distribution graph using discrete values. As interpreted by the examiner, Jang teaches method 3 having a sufficiently larger clock cycle than dark current pulse (as dark count) generation period, meaning that there's a large sample size of the dark current pulses (as dark counts). 1Furthermore, Jang teaches the dark current pulse (as dark count) frequency following a Poisson distribution, which with the large sample size, resembles a normal distribution).
With regards to claim 8, Jang teaches:
A device for generating a random number, (¶[0001] regarding a random number generator
using dark current (as dark count));
the device comprising: a single photon detector (SPD) detecting a dark count; (Fig. 3 items 100 and Dark Current Pulse; ¶[0001] regarding using a single photon avalanche diode (as a single photon detector) for generating a dark current pulse (as a dark count) for random number generation);
and a processor configured to recognize the number of detection times at which the single photon detector detects the dark count, (Fig. 6 regarding method 3 M-bit Counter (as a processor); Fig. 3 items 100 and Dark Current Pulse (as a dark count); ¶[0001]regarding using a single photon avalanche diode (as a single photon detector) for generating a dark current pulse (as a dark count) for random number generation; ¶[0033] regarding an M-bit pulse counter incrementing the counter value every dark current pulse);
and to generate a random number based on a bit value allocated to a section including the number of detection times among a plurality of sections, (Fig. 3 items 300, Dark Current Pulse, 400, and M-bits digital signal; ¶[0001] regarding a random number generator using dark current (as dark count); ¶[0030] regarding the pulse-random number converter (Fig. 3 item 400) generating M-bit digital random numbers using method 3, the number of dark current pulses (as dark counts generated within a certain time period;¶[0033] regarding method 3 incrementing a counter for each dark current pulse with the counter output value following Poisson distribution with the dark pulse (as a dark count) frequency for a given period as a parameter; ¶[0034 regarding method 3 having a sufficiently larger clock cycle than the dark current pulse generation period, and an example output probability falling within generating a '0' or '1' (as a plurality of sections); As interpreted by the Examiner, [0033]-[0034] describes method 3 increasing a count whenever a dark count comes in. 1Furthermore it states that the output, as in the output random numbers, follow a Poisson distribution. [0034] describes an example of a 1-bit value output in the Poisson distribution with an even probability of '0' or '1' output. As interpreted by the Examiner, what is being described is two sections in a Poisson distribution, with outputs '0' and '1' assigned as separate "sections" for which the number of dark counts to fall in);
wherein the section refers to a group of values and the sections refer to groupings of values, (¶[0030] regarding the pulse-random number converter (Fig. 3 item 400) generating M-bit digital random numbers using method 3, the number of dark current pulses (as dark counts) generated within a certain time period; ¶[0033] regarding method 3 incrementing a counter for each dark current pulse with the counter output value following 1Poisson distribution with the dark pulse (as a dark count) frequency for a given period as a parameter; ¶[0034] regarding method 3 having a sufficiently larger clock cycle than the dark current pulse generation period, and an example output probability falling within generating a '0' or '1' (as a plurality of sections). As interpreted by the Examiner, [0030] describes counting a plurality of dark current pulses generated in a certain time period, and [0034] describes an example output of '0' or '1' based on the number of dark counts (as a group of values for a section outputting '0' and a group of values for a section outputting '1'));
wherein each of the plurality of sections corresponds to an equiprobable partition of a normal distribution of dark count detection values such that a probability that the dark count is detected in each section is substantially equal to 1/2N, (¶[0034] regarding an example of a bit size of 1 and an example output probability falling within generating a '0' (as a first section) or '1' (as a second section) with the probability for each section being 50% As interpreted by the Examiner, having a bit size of 1 indicates the need for two sections (in this example, one section for ‘0’, and another for ’1’). Furthermore, as interpreted by the Examiner, having a bit size of 1 indicates that the probability for each section would need to be 1/21 (or 50%) according to 1/2N);
and wherein the number of sections (2N) is determined based on a bit size N. (¶[0034] regarding an example of a bit size of 1 and an example output probability falling within generating a '0' (as a first section) or '1' (as a second section) with the probability for each section being 50%; As interpreted by the Examiner, having a bit size of 1 indicates the need for two sections (in this example, one section for ‘0’, and another for ’1’) which falls in line with 2N.).
Jang does not explicitly teach:
bit size set by a user
However, Lu teaches:
bit size set by a user (¶[0045] regarding the user specifying the number of bits generated by a random number generator)
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine the teachings of Jang with the bit size set by a user of LU because it allows the output to be the “desired number of random bits” [LU : ¶[0045]].
With regards to claim 10, Jang in view of Lu teaches the device of claim 8, as referenced above.
Jang further teaches:
wherein the single photon detector detects whether the dark count occurs when a trigger signal is applied, by the processor, at a predetermined time interval. (Fig. 6 regarding the RNG generation clock (the low logic value being considered the trigger signal); Fig. 7 regarding method 3’s dark current pulse (as dark count), and RNG generation clock (the low logic value being considered the trigger signal); ¶[0001] regarding the single photon avalanche diode (as a single photon detector) detects dark current and generates dark current pulses (as dark counts generated within a predetermined time interval; Fig. 6 regarding method 3 M-bit Counter (as a processor); ¶[0033] regarding an M-bit pulse counter (as a processor) incrementing the counter value every dark current pulse).
With regards to claim 11, Jang in view of Lu teaches the device of claim 8, as referenced above.
Jang further teaches:
wherein a probability value that the dark count will be detected by each real value included in each of the plurality of sections follows a normal distribution. (¶[0033] regarding method 3 of the random number generator generating random values based on dark current pulses (as dark counts) within a given time period. Furthermore, regarding the average dark pulse (as dark count) frequency in a given time period following a Poisson distribution; ¶[0034] regarding method 3 having a sufficiently larger clock cycle than the dark current pulse generation period, and an example output probability falling within generating a '0' (as a first section) or '1' (as a second section) with the probability for each section being 50% so there is no possibility that the probability is concentrated; As noted by the examiner, normal distribution is used for parameters that have a continuous value, whereas counting dark counts are of discrete values. Furthermore, Poisson distribution, with a large enough sampling size, resembles a normal distribution graph using discrete values. As interpreted by the examiner, Jang teaches method 3 having a sufficiently larger clock cycle than dark current pulse (as dark count) generation period, meaning that there's a large sample size of the dark current pulses (as dark counts). 1Furthermore, Jang teaches the dark current pulse (as dark count) frequency following a Poisson distribution, which with the large sample size, resembles a normal distribution).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEROME ANTHONY KLOSTERMAN II whose telephone number is (571)272-0541. The examiner can normally be reached Monday - Friday 8:30am ET - 3:30pm ET.
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/J.A.K./Examiner, Art Unit 2182 /EMILY E LAROCQUE/ Primary Examiner, Art Unit 2182
1 Differences between the normal and Poisson distributions. The Analysis Factor RSS. (2017).
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