DETAILED ACTION
Applicant’s response filed 12/03/2025 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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 Status
Claims 3-5 and 14 are cancelled by Applicant.
Claims 1-2, 6-13 and 15-17 are currently pending and are herein under examination.
Claims 1-2, 6-13 and 15-17 are rejected.
Claims 1, 7 and 15-16 are objected.
Priority
The instant application claims domestic benefit to international application PCT/JP2019/043368 filed 11/06/2019, which claims the benefit of priority to Japanese application No. 2018-215619 filed 11/16/2018. The claim to the benefit of priority for claims 1-2, 6-13 and 15-17 is acknowledged. As such, the effective filing date for claims 1-2, 6-13 and 15-17 is 11/16/2018.
Claim Objections
The objection to claims 7, 10 and 16-17 is withdrawn in view of Applicant’s claim amendments.
Claims 1, 7 and 15-16 are objected to because of the following informalities:
Claim 1, line 5, recites the phrase “a set of pieces of spectral data of the plurality of pieces” which should be “a set of pieces of spectral data from the plurality of pieces”.
Claim 1, line 16, recites the phrase “of the each cluster” which should be “of each cluster”.
Claim 1, lines 19-20, recites the phrase “of the each cluster” which should be “of each cluster”.
Claim 1, lines 22-23, recites the phrase “of the each cluster” which should be “of each cluster in the plurality of clusters”.
Claim 7, line 3, recites the phrase “of the each cluster” which should be “of each cluster”.
Claim 15, line 4, recites the phrase “a set of pieces of spectral data of the plurality of pieces” which should be “a set of pieces of spectral data from the plurality of pieces”.
Claim 15, line 15, recites the phrase “controlling display device” which should be “controlling a display device”.
Claim 15, lines 15-16, recites the phrase “of the each cluster” which should be “of each cluster”.
Claim 15, line 19, recites the phrase “of the each cluster” which should be “of each cluster”.
Claim 15, line 22, recites the phrase “of the each cluster” which should be “of each cluster in the plurality of clusters”.
Claim 16, line 6, recites the phrase “a set of pieces of spectral data of the plurality of pieces” which should be “a set of pieces of spectral data from the plurality of pieces”.
Claim 16, lines 16-17, recites the phrase “of the each cluster” which should be “of each cluster”.
Claim 16, line 20, recites the phrase “of the each cluster” which should be “of each cluster”.
Claim 16, lines 23-24, recites the phrase “of the each cluster” which should be “of each cluster in the plurality of clusters”.
Appropriate correction is required.
Withdrawn Rejections
35 USC 112(b)
The rejection of claims 1-3 and 6-17 under 35 USC 112(b) is withdrawn in view of Applicant’s claim amendments.
35 USC 103
The rejection of claims 1-3, 6 and 13-17 under 35 U.S.C. 103 as being unpatentable over Jimenez-Carretero et al. in view of Strehl et al., and Bruggner et al. is withdrawn in view of claim amendments.
The rejection of claims 7-8 and 10 under 35 U.S.C. 103 as being unpatentable over Jimenez-Carretero et al. in view of Strehl et al., Bruggner et al., Boedigheimer et al., and wflynny is withdrawn in view of claim amendments.
The rejection of claim 9 under 35 U.S.C. 103 as being unpatentable over Jimenez-Carretero et al. in view of Strehl et al., Bruggner et al., Boedigheimer et al., wflynny, and MicroStrategy is withdrawn in view of claim amendments.
The rejection of claims 11 and 12 under 35 U.S.C. 103 as being unpatentable over Jimenez-Carretero et al. in view of Strehl et al., Bruggner et al., Pouyan et al., and Wikipedia is withdrawn in view of claim amendments.
Claim Rejections - 35 USC § 112
35 USC 112(b)
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 1-2, 6-13 and 15-17 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. This rejection is newly recited and is necessitated by claim amendment.
Claim 1, line 17, recites the phrase “the calculated evaluated value” which renders the claim indefinite. It is unclear which calculated evaluated value is being referenced because there is a calculated evaluated value for each cluster, as recited in lines 15-16. To overcome this rejection, clarify which value is being referenced.
Claim 1, line 21, recites the phrase “the calculated evaluated value that is negative” which lacks antecedent basis. There is no prior recitation of a negative calculated evaluated value. To overcome this rejection, provide antecedent basis for the phrase.
Furthermore, claims 2, 6-13 and 17 are also rejected because they depend on claim 1, which is rejected, and because they do not resolve the issue of indefiniteness.
Claim 15, line 17, recites the phrase “the calculated evaluated value” which renders the claim indefinite. It is unclear which calculated evaluated value is being referenced because there is a calculated evaluated value for each cluster, as recited in lines 15-16. To overcome this rejection, clarify which value is being referenced.
Claim 15, line 21, recites the phrase “the calculated evaluated value that is negative” which lacks antecedent basis. There is no prior recitation of a negative calculated evaluated value. To overcome this rejection, provide antecedent basis for the phrase.
Claim 16, line 18, recites the phrase “the calculated evaluated value” which renders the claim indefinite. It is unclear which calculated evaluated value is being referenced because there is a calculated evaluated value for each cluster, as recited in lines 16-17. To overcome this rejection, clarify which value is being referenced.
Claim 16, line 22, recites the phrase “the calculated evaluated value that is negative” which lacks antecedent basis. There is no prior recitation of a negative calculated evaluated value. To overcome this rejection, provide antecedent basis for the phrase.
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 1-2, 6-13 and 15-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Any newly recited portions here are necessitated by claim amendment.
Step 2A, Prong 1:
In accordance with MPEP § 2106, claims found to recite statutory subject matter (Step 1: YES) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomena (Step 2A, Prong 1). In the instant application, claims 1-2, 6-13 and 17 are directed to an apparatus, claim 15 is directed to a method, and claim 16 is directed to a product; all of which are statutory categories of inventions. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claim 1 recites “select a set of pieces of spectral data of the plurality of pieces of spectral data based on a first user input; compress the selected set of pieces of spectral data; acquire, based on the transmission, a result of a clustering process of the compressed set of pieces of spectral data; calculate, based on the result of the clustering process an evaluated value for each cluster of a plurality of clusters by a calculation of an average for each cluster of the plurality of clusters; wherein the calculated evaluated value includes Clustering Validity Indices (CVI), and the CVI is a value based on a degree of separation of the each cluster in the plurality of clusters; and change, based on the calculated evaluated value that is negative, a parameter of the clustering process prior to additional analysis of the each cluster.”
Claim 6 recites “determine, based on the result of the clustering process, a degree of deviation of each piece of spectral data of the acquired plurality of pieces of spectral data.”
Claim 7 recites “determine the degree of deviation of the each piece of spectral data of the acquired plurality of pieces of spectral data is one of: equal to or larger than a threshold, or smaller than the threshold;”
Claim 15 recites “selecting … a set of pieces of spectral data of the plurality of pieces of spectral data based on a first user input; compressing … the selected set of pieces of spectral data; acquiring … based on the transmission, a result of a clustering process of the compressed set of pieces of spectral data; calculating … based on the result of the clustering process an evaluated value for each cluster of a plurality of clusters by a calculation of an average for each cluster of the plurality of clusters; wherein the calculated evaluated value includes Clustering Validity Indices (CVI), and the CVI is a value based on a degree of separation of the each cluster in the plurality of clusters; and change, based on the calculated evaluated value that is negative, a parameter of the clustering process prior to additional analysis of the each cluster.”
Claim 16 recites “selecting a set of pieces of spectral data of the plurality of pieces of spectral data based on a first user input; compressing the selected set of pieces of spectral data; acquiring, based on the transmission, a result of a clustering process of the compressed set of pieces of spectral data; calculating, based on the result of the clustering process an evaluated value for each cluster of a plurality of clusters by a calculation of an average for each cluster of the plurality of clusters; wherein the calculated evaluated value includes Clustering Validity Indices (CVI), and the CVI is a value based on a degree of separation of the each cluster in the plurality of clusters; and change, based on the calculated evaluated value that is negative, a parameter of the clustering process prior to additional analysis of the each cluster”
Claim 17 recites “determine one-to-one correspondence between the compressed set of pieces of spectral data and the selected set of pieces of spectral data.”
Limitations reciting a mental process.
The above cited limitations in claims 1, 6-7 and 15-17 are recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. Below is an analysis of the limitations reciting a mental process under their broadest reasonable interpretation (BRI):
The following limitations recited above in claims 1, 6-7 and 15-17 recite a mental process: selecting a set of pieces of spectral data, compressing the selected set of pieces of spectral data, calculating an evaluated value for each cluster by calculating an average for each cluster which includes Cluster Validity Indices based on a degree of separation, changing a parameter of a clustering process, and determining a one-to-one correspondence between compressed and selected spectral data. The BRI of performing a compression process includes performing a lossless data compression algorithm that identifies and removes redundancy in a dataset, which a human could perform by following the steps of the algorithm. A human can perform other mathematical operations such as calculating degrees of deviation as well perform the calculations of a silhouette coefficient equation as recited in specification para. [83-85]. A human can make determinations regarding analysis of data. A human can look at the compressed data and selected data and determine which pieces of data correspond to one another by analyzing the data. Therefore, these limitations fall under the “Mental Process” grouping of abstract ideas.
Limitations reciting a mathematical concept.
The above cited limitations in claims 1, 6 and 15-16 equate to a mathematical concept because these limitations are similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts.
Regarding the above cited limitations in claims 1, 6, and 15-16 of compressing spectral data, calculating an evaluated value for each cluster by calculating an average for each cluster which includes Cluster Validity Indices based on a degree of separation, and calculating a degree of deviation. These limitations equate to a mathematical concept because they require using mathematical operations/functions to derive a numerical value. For instance, the BRI of compressing data includes using a lossless data compression function such as Run-Length Encoding. The BRI of a clustering process includes using k-means clustering. The BRI of calculating an evaluated value that includes a CVI includes performing the calculations of a silhouette coefficient equation as recited in specification para. [83-84]. Therefore, these limitations fall under the “Mathematical concept” grouping of abstract ideas.
As such, claims 1-2, 6-13 and 15-17 recite an abstract idea (Step 2A, Prong 1: Yes).
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)) and to insignificant extra-solution activity (MPEP § 2106.05(g)). The instant claims recite the following additional elements:
Claim 1 recites “a central processing unit (CPU) configured to: acquire a plurality of pieces of spectral data based on irradiation of a plurality of particles with a laser beam; transmit the compressed set of pieces of spectral data via a network; control a display device to superimpose the calculated evaluated value of the each cluster on the result of the clustering process.”
Claim 2 recites “The information processing apparatus according to claim 1, wherein the CPU is further configured to: control the display device to display the result of the clustering process based on the selected set of pieces of spectral data.”
Claim 6 recites “The information processing apparatus according to claim 1, wherein the CPU is further configured to …”
Claim 7 recites “The information processing apparatus according to claim 6, … the CPU is further configured to: control, based on the determination that the degree of deviation of a first piece of spectral data of the acquired plurality of pieces of spectral data is equal to or larger than the threshold, the display device to display the first piece of spectral data in a first display form; and control, based on the determination that the degree of deviation of a second piece of spectral data of the acquired plurality of pieces of spectral data is smaller than the threshold, the display device to display the second piece of spectral data in a second display form, and the second display form is different from the first display form.”
Claim 8 recites “The information processing apparatus according to claim 7, wherein the CPU is further configured to control the display device to display the first piece of spectral data in a different color from the second piece of spectral data.”
Claim 9 recites “The information processing apparatus according to claim 7, wherein the CPU is further configured to control the display device to display the first piece of spectral data with different transparency from the second piece of spectral data.”
Claim 10 recites “The information processing apparatus according to claim 7, wherein the CPU is further configured to dynamically change the display of the first piece of spectral data and the display of the second piece of spectral data, based on a dynamic change of the threshold.”
Claim 11 recites “The information processing apparatus according to claim 1, wherein the CPU is further configured to transmit the compressed set pieces of spectral data to a device, and the device is connected to the information processing apparatus via the network.”
Claim 12 recites “The information processing apparatus according to claim 11, wherein the CPU is further configured to acquire the result of the clustering process from the device.”
Claim 13 recites “The information processing apparatus according to claim 1, wherein the plurality of pieces of spectral data is output from a flow cytometer.”
Claim 15 recites “acquiring, by a central processing unit (CPU), a plurality of pieces of spectral data based on irradiation of a plurality of particles with a laser beam; by the CPU; transmitting, by the CPU, the compressed set of pieces of spectral data via a network; by the CPU; by the CPU; and controlling display device to superimpose the calculated evaluated value of the each cluster on the result of the clustering process.”
Claim 16 recites “A non-transitory computer-readable medium having stored thereon, computer-executable instructions which, when executed by a processor, cause the processor to execute operations, the operations comprising: acquiring a plurality of pieces of spectral data based on irradiation of a plurality of particles with a laser beam; transmitting the compressed set of pieces of spectral data via a network; controlling a display device to superimpose the calculated evaluated value of the each cluster on the result of the clustering process.”
Claim 17 recites “The information processing apparatus according to claim 1, wherein the CPU is further configured to …”
Regarding the above cited limitations in claims 1-2, 6-13 and 15-17 of the information processing apparatus that comprises a CPU, the display device, the device, the computer-readable medium, and the processor. These limitations appear to be components of a generic computer and of a generic computing system, and there are no limitations requiring anything other than a generic computing system. This interpretation is reinforced by Figure 1 and specification para. [26] which state that a computer performs the functions of the information processing apparatus, which operates independently of a flow cytometer. Similarly, the broadest reasonable interpretation of “the display device” and “the device” includes them being a generic computer. Therefore, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Regarding the above cited limitations in claims 1-2, 7-13 and 15-16 of acquiring data, transmitting data, and displaying data on a display device, these limitations equate to insignificant, extra-solution activity of mere necessary data gathering and outputting.
As such, claims 1-2, 6-13 and 15-17 are directed to an abstract idea (Step 2A, Prong 2: No).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The instant claims recite the following additional elements:
Claim 1 recites “a central processing unit (CPU) configured to: acquire a plurality of pieces of spectral data based on irradiation of a plurality of particles with a laser beam; transmit the compressed set of pieces of spectral data via a network; control a display device to superimpose the calculated evaluated value of the each cluster on the result of the clustering process.”
Claim 2 recites “The information processing apparatus according to claim 1, wherein the CPU is further configured to: control the display device to display the result of the clustering process based on the selected set of pieces of spectral data.”
Claim 6 recites “The information processing apparatus according to claim 1, wherein the CPU is further configured to …”
Claim 7 recites “The information processing apparatus according to claim 6, … the CPU is further configured to: control, based on the determination that the degree of deviation of a first piece of spectral data of the acquired plurality of pieces of spectral data is equal to or larger than the threshold, the display device to display the first piece of spectral data in a first display form; and control, based on the determination that the degree of deviation of a second piece of spectral data of the acquired plurality of pieces of spectral data is smaller than the threshold, the display device to display the second piece of spectral data in a second display form, and the second display form is different from the first display form.”
Claim 8 recites “The information processing apparatus according to claim 7, wherein the CPU is further configured to control the display device to display the first piece of spectral data in a different color from the second piece of spectral data.”
Claim 9 recites “The information processing apparatus according to claim 7, wherein the CPU is further configured to control the display device to display the first piece of spectral data with different transparency from the second piece of spectral data.”
Claim 10 recites “The information processing apparatus according to claim 7, wherein the CPU is further configured to dynamically change the display of the first piece of spectral data and the display of the second piece of spectral data, based on a dynamic change of the threshold.”
Claim 11 recites “The information processing apparatus according to claim 1, wherein the CPU is further configured to transmit the compressed set pieces of spectral data to a device, and the device is connected to the information processing apparatus via the network.”
Claim 12 recites “The information processing apparatus according to claim 11, wherein the CPU is further configured to acquire the result of the clustering process from the device.”
Claim 13 recites “The information processing apparatus according to claim 1, wherein the plurality of pieces of spectral data is output from a flow cytometer.”
Claim 15 recites “acquiring, by a central processing unit (CPU), a plurality of pieces of spectral data based on irradiation of a plurality of particles with a laser beam; by the CPU; transmitting, by the CPU, the compressed set of pieces of spectral data via a network; by the CPU; by the CPU; and controlling display device to superimpose the calculated evaluated value of the each cluster on the result of the clustering process.”
Claim 16 recites “A non-transitory computer-readable medium having stored thereon, computer-executable instructions which, when executed by a processor, cause the processor to execute operations, the operations comprising: acquiring a plurality of pieces of spectral data based on irradiation of a plurality of particles with a laser beam; transmitting the compressed set of pieces of spectral data via a network; controlling a display device to superimpose the calculated evaluated value of the each cluster on the result of the clustering process.”
Claim 17 recites “The information processing apparatus according to claim 1, wherein the CPU is further configured to …”
Regarding the above cited limitations in claims 1-2, 6-13 and 15-17 of the information processing apparatus that comprises a CPU, the display device, the device, the computer-readable medium, and the processor, these limitations appear to be components of a generic computer and of a generic computing system, and there are no limitations requiring anything other than a generic computing system. This interpretation is reinforced by Figure 1 and specification para. [26] which states that a computer performs the functions of the information processing apparatus, which operates independently of a flow cytometer. Similarly, the broadest reasonable interpretation of “the display device” and “the device” includes them being a generic computer. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computing system, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Storing code in a non-transitory computer readable medium as stated in claim 16 equates to storing information in memory, which the courts have established as a WURC function of a generic computer in Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Regarding the above cited limitations in claims 1-2, 7-12 and 15-16 of acquiring data, outputting data, and displaying the outputted data, these limitations read on receiving/transmitting data over a network, which the courts have established as WURC function of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014). Furthermore, claim 13 also equates to transmitting/receiving data because it limits the type of data being transmitted/received.
When these additional elements are considered individually and in combination, they amount to WURC generic computer functions/components and therefore do not comprise an inventive concept that transforms the claimed judicial exception into a patent-eligible application of the judicial exception itself (Step 2B: No).
As such, claims 1-2, 6-13 and 15-17 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 12/03/2025 have been fully considered but they are not persuasive.
Applicant argues that certain limitations in claim 1 are not mental processes because they are inextricably tied to a machine (pg. 10, para. 4 – pg. 11, para. 1 of Applicant’s remarks). Applicant’s argument is persuasive in part for the following reason:
The following limitations listed by Applicant recite a mental process even though they are performed by a CPU: “calculate, based on the result of the clustering process an evaluated value for each cluster of a plurality of clusters by a calculation of an average for each cluster of the plurality of clusters” and “change, based on the calculated evaluated value that is negative, a parameter of the clustering process prior to additional analysis of the each cluster.” MPEP 2106.04(a)(2)(III)(C) recites that performing a mental process in a computer still recites a mental process.
Applicant argues that claim 1 contains a practical application of improving information processing technology by calculating an evaluated value (i.e., average) for each cluster (pg. 11, para. 2 – pg. 13, para. 1 of Applicant’s remarks). Applicant’s argument is not persuasive for the following reasons:
Applicant appears to argue for improving computer functionality. The alleged improvement appears to be derived from the following claim 1 limitations: “calculate, based on the result of the clustering process, an evaluated value for each cluster of a plurality of clusters by a calculation of an average for each cluster of the plurality of clusters.” This limitation in claim 1 has been identified as reciting a mental process and a mathematical concept. MPEP 2106.05(a) recites “the judicial exception alone cannot provide the improvement” when evaluating improvements in computer functionality. As such, claim 1 does not contain an improvement in computer functionality.
Applicant appears to argue the amended limitations of claim 1 are not well-understood, routine, and conventional (WURC) (pg. 13, para. 2 of Applicant’s remarks). Applicant’s argument is not persuasive for the following reasons:
Under Step 2B, only additional elements are evaluated for whether they are WURC. The only additional element out of the amended limitations in claim 1 is “control a display device to superimpose the calculated evaluated value of the each cluster on the result of the clustering process.” However, this additional element has been identified as a generic computer function, which is WURC.
Applicant argues that claims 15 and 16 are patent eligible for the same reasons described above for claim 1 (pg. 13, last para. of Applicant’s remarks). However, the arguments for claim 1 are not persuasive and are thus not persuasive for claims 15 and 16.
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 6, 13 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Jimenez-Carretero et al. (NPL ref. 2 in IDS filed 10/05/2022; The Journal of Immunology 200, no. 10 (2018): 3319-3331; previously cited) in view of Rezankova (In 21st international scientific conference AMSE applications of mathematics and statistics in economics, pp. 1-10. 2018; newly cited).
This rejection is newly recited and is necessitated by claim amendment.
The bold and italicized text below are the limitations of the instant claims, and the italicized text serves to map the prior art onto the instant claims.
Claims 1 and 15-16:
An information processing apparatus, comprising: a central processing unit (CPU) configured:
An information processing method, comprising:
A non-transitory computer-readable medium having stored thereon, computer-executable instructions which, when executed by a processor, cause the processor to execute operations, the operations comprising:
Jimenez-Carretero discloses methods for flow cytometry preparation for improved automated phenotypic analysis and use various computer-implemented software and online scripts (pg. 3321, col. 1, para. 6) (pg. 3321, col. 2, paras. 2-3) (abstract). Thus, the method is computer-implemented which necessitates use of a computer that contains a processor and memory.
acquire a plurality of pieces of spectral data based on irradiation of a plurality of particles with a laser beam;
Jimenez-Carretero used a SP6800 Spectral Cell Analyzer flow cytometer equipped with three lasers and 68 detectors to collect spectral flow cytometry (FCM) data (pg. 3320, col. 2, para. 1).
select a set of pieces of spectral data of the plurality of pieces of spectral data based on a first user input;
Jimenez-Carretero teaches subsampling the spectral FCM data: “A subsampling procedure was applied to each sample to reduce the number of events bound for the posterior clustering and dimensionality-reduction steps … In all experiments, data were downsampled to 2500 events per sample by using default parameters for the algorithm and all channels involved in the specific data input mode” (pg. 3320, col. 2, para. 5).
compress the selected set of pieces of spectral data;
Jimenez-Carretero teaches dimensionality reduction of the downsampled spectral FCM data: “To visualize population representations and clustering results, we used the Barnes-Hut t-Distributed Stochastic Neighbor Embedding (t-SNE) (15) implementation in R (https://github.com/jkrijthe/ Rtsne) with the recommended parameters (perplexity 5 30, u 5 0.5, iterations 5 1000, Euclidean distance). Because the t-SNE method is completely data dependent and provides no explicit function for projections, different runs and different samples report different outputs. For this reason, events from all downsamples were processed together, providing a common t-SNE map distribution allowing sample comparison” (pg. 3321, col. 1, para. 2).
transmit the compressed set of pieces of spectral data via a network;
The broadest reasonable interpretation of transmitting compressed spectral data across a network includes transferring data within a singular computer system using internal buses. Jimenez-Carretero teaches that spectral data was dimensionality reduced in R using a Barnes-Hut t-Distributed Stochastic Neighbor Embedding, which were then used to perform clustering in Phenograph which is another R application (pg. 3321, para. 2-3). Therefore, data is transferred along the internal bus of the computer used in Jimenez-Carretero in order to perform the data analysis steps, which under its broadest reasonable interpretation is an internal network. This interpretation is reinforced by the fact that the claim does not specify that the network be an external network.
acquire, based on the transmission, a result of a clustering process of the compressed set of pieces of spectral data;
Jimenez-Carretero teaches automatic clustering of the downsampled spectral FCM data: “All subsampled events from the whole sample set were used jointly for unbiased clustering, providing a common detection of subpopulations that allowed sample comparison. We tested automatic clustering algorithms recommended by Weber and Robinson (16) that allowed runs in script mode and required no prior information about the size or number of expected clusters. We also tested algorithms relying on t-SNE projections. All algorithms tested, including PhenoGraph (17), DenseVM (18), ClusterX (19), and flowMeans (20), were run in R with the default settings. PhenoGraph was selected as the default clustering method in the automatic analysis workflow for further experiments because it reported the best lower 99% confident interval of F1 scores (see Materials and Methods, Evaluation of clustering results) when analyzing conventional FCM data input in the traditional way (CONV_4ch_CC) using the reference dataset (Supplemental Fig. 2)” (pg. 3321, col. 2, para. 3).
calculate, based on the result of the clustering process, an evaluated value for each cluster of a plurality of clusters by a calculation of an average for each cluster of the plurality of clusters;
Jimenez-Carretero teaches automatic clustering of the downsampled spectral FCM data (pg. 3321, col. 2, para. 3) (Figure 1D). However, Jimenez-Carretero does not calculate an evaluated value for each cluster by calculating an average for each cluster.
Rezankova teaches calculations of a silhouette coefficient used for cluster evaluation (abstract). Rezankova recites “For the calculation of the silhouette coefficient, for each i-th object, which is an element of the cluster Cg, there is computed the value (the width of the rectangle in the silhouette plot)
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… The silhouette coefficient is the average of values SWi” (pg. 261, sec. 2.2).
control a display device to superimpose the calculated evaluated value of the each cluster on the result of the clustering process,
Jimenez-Carretero displays clusters of the spectral data using MATLAB (pg. 3321, col. 1, sec. Software resources) (Figure 2 & 4). However, Jimenez-Carretero does not associate evaluated values of each cluster with the clusters themselves.
Rezankova recites “In Table 3 we can see values of the silhouette coefficient obtained by the R environment and the averages of silhouette values computed for each object with the STATS CLUS SIL command for all three clustering algorithms” (pg. 265, last para.).
wherein the calculated evaluated value includes Clustering Validity Indices (CVI), and the CVI is a value based on a degree of separation of the each cluster in the plurality of clusters; and
Jimenez-Carretero shows the clusters in Figures 1-2 and 4. However, Jimenez-Carretero does not calculate a CVI based on degree of separation of each cluster.
Rezankova discloses the same silhouette coefficient described in instant specification para. [83-85]. Thus, the description of the silhouette coefficient described in instant specification para. [85] applies to Rezankova, which is a CVI based on a degree of separation.
change, based on the calculated evaluated value that is negative, a parameter of the clustering process prior to additional analysis of the each cluster.
Jimenez-Carretero shows in Figure 1D the clustering workflow (pg. 3321, col. 1, para. 3). However, Jimenez-Carretero does not change a parameter of the automated clustering based on an evaluated value that is negative.
Rezankova teaches “The silhouette coefficient takes on values from the interval [−1, 1]. The higher value means the better assignment of objects into clusters. The suitable number of clusters can be determined on the basis of the highest value (usually within a certain interval of the numbers)” (pg. 261, sec. 2.2).
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Jimenez-Carretero for automated phenotyping of cell populations using clustering by calculating silhouette coefficients for the clusters and performing additional clustering when a silhouette coefficient is negative as taught by Rezankova. Motivation for doing is taught by Rezankova who states that the silhouette coefficient determines a suitable number of clusters, wherein a positive silhouette coefficient equals better assignment of objects into clusters (i.e., spectral data into cell populations for Jimenez-Carretero). One of ordinary skill in the art would have had a reasonable expectation of success for calculating a silhouette coefficient to evaluate clustering results in Jimenez-Carretero because Jimenez-Carretero performs clustering. Additionally, Rezankova recites that the silhouette function is often applied as an index and its calculation is well-known (pg. 259, para. 1).
Claim 2:
Jimenez-Carretero displays the results of the automated clustering of the downsamples in Figures 1 & 2.
Claim 6:
Jimenez-Carretero clusters spectral data from a flow cytometer (pg. 3321, col. 1, para. 3). However, Jimenez-Carretero does not teach calculating a degree of variation for the clusters.
Rezankova discloses the silhouette coefficient as equation (4) on pg. 261, sec. 2.2. As discussed in instant specification para. [85], the variable bi indicates a degree of deviation from the non-belonging neighboring clusters.
Claim 13:
Jimenez-Carretero states that their data is collected from a spectral flow cytometer (pg. 3320, col. 2, para. 1).
Claim 17:
Jimenez-Carretero teaches “The Hungarian assignment algorithm was used to solve the problem of mapping one–to–one clusters and reference populations by maximizing the sum of F1 scores across all reference classes” (pg. 3321, col. 1, last para.).
Claims 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Jimenez-Carretero et al. (NPL ref. 2 in IDS filed 10/05/2022; The Journal of Immunology 200, no. 10 (2018): 3319-3331; previously cited) in view of Rezankova (In 21st international scientific conference AMSE applications of mathematics and statistics in economics, pp. 1-10. 2018; newly cited), as applied above to claims 1 and 6, and in further view of SciKit (Selecting the number of clusters with silhouette analysis on KMeans clustering; published online 2016; newly cited) and wflynny (How to put colours in dendograms of matplotlib, pg. 1-7; published 2013; previously cited on PTO892 mailed 07/17/2024).
This rejection is newly recited and is necessitated by claim amendment.
The limitations of claims 1 and 6 have been taught in the rejection above by Jimenez-Carretero and Rezankova.
Claims 7-8:
wherein the determined degree of deviation of the each piece of spectral data of the acquired plurality of pieces of spectral data is one of: equal to or larger than a threshold, or smaller than the threshold,
Jimenez-Carretero clusters spectral data from a flow cytometer (pg. 3321, col. 1, para. 3). Rezankova discloses the silhouette coefficient as equation (4) on pg. 261, sec. 2.2. As discussed in instant specification para. [85], the variable bi indicates a degree of deviation from the non-belonging neighboring clusters.
However, neither Jimenez-Carretero or Rezankova determine that the bi variable for each spectral datapoint is smaller or larger than a threshold.
Scikit discloses selecting a number of clusters with a silhouette analysis (title). Scikit teaches “Silhouette coefficients (as these values are referred to as) near +1 indicate that the sample is far away from the neighboring clusters. A value of 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters and negative values indicate that those samples might have been assigned to the wrong cluster” (pg. 1, para. 2).
It would have been prima facie obvious to one of ordinary in the art to have modified the method of Jimenez-Carretero and Rezankova to determine whether the silhouette coefficient of each spectral datapoint in a cluster was above or below a threshold of 0, as taught by Scikit, in order to display which spectral datapoints are closer to +1 (i.e., far away from neighboring clusters). One of ordinary skill in the art would have had a reasonable expectation of success because the silhouette coefficient calculates the degree of deviation.
control, based on the determination that the degree of deviation of a first piece of spectral data of the acquired plurality of pieces of spectral data is equal to or larger than the threshold, the display device to display the first piece of spectral data in a first display form; and control, based on the determination that the degree of deviation of a second piece of spectral data of the acquired plurality of pieces of spectral data is smaller than the threshold, the display device to display the second piece of spectral data in a second display form, and the second display form is different from the first display form.
Jimenez-Carretero teaches displaying the clusters with spectral data (Figures 1 and 2).
Rezankova discloses the silhouette coefficient as equation (4) on pg. 261, sec. 2.2. As discussed in instant specification para. [85], the variable bi indicates a degree of deviation from the non-belonging neighboring clusters. Jimenez-Carretero, Rezankova, and Scikit together teach determining whether datapoints are smaller or larger than a threshold.
However, Jimenez-Carretero, Rezankova, and Scikit do not teach displaying datapoints that are smaller than the threshold in a different form from datapoints that are larger than the threshold.
wflynny discloses code allowing data greater than or equal to a threshold to be a distinct color from data below the threshold (pg. 1, second para. from the bottom). The Figure on pg. 2 shows that the links connecting nodes greater than or equal to a 1 threshold are in blue, whereas everything below the threshold is a different color.
It would have been prima facie obvious to one of ordinary skill in the art to have given different colors to spectral data in Jimenez-Carretero, Rezankova, and Scikit that was smaller and greater than a threshold as taught by wflynny. The motivation for doing so is to distinguish spectral data that has a negative versus positive silhouette coefficient. There would have been a reasonable expectation of success because wflynny provides code to set thresholds and change colors for plotting.
Claim 9:
The limitations of claim 9 are being interpreted as a design choice, and Applicant has not disclosed that this feature provides an advantage, is used for a particular purpose, or solves a stated problem when compared to changing color between datapoints that fall below or above a threshold, as taught above by wflynny. Therefore, changing color as taught by wflynny would perform equally as changing transparency to distinguish different types of data, and such a modification fails to patentably distinguish over wflynny.
Claim 10:
Jimenez-Carretero displays the clusters with spectral data (Figures 1 and 2). Jimenez-Carretero, Rezankova, and Scikit together teach determining whether datapoints are equal to or smaller than a silhouette coefficient threshold.
However, Jimenez-Carretero, Rezankova, and Scikit do not teach changing the display based on a dynamic change of the threshold.
wflynny teaches that changing the threshold will alter the color of the nodes (pg. 3).
It would have been prima facie obvious to one of ordinary skill in the art to have modified Jimenez-Carretero, Rezankova, and Scikit by altering the color of the spectral data when dynamically changing a threshold as taught by wflynny in order to see changes in real-time. There would have been a reasonable expectation of success because wflynny provides an example of dynamic threshold change and real-time updating of data colors (pg. 3).
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Jimenez-Carretero et al. (NPL ref. 2 in IDS filed 10/05/2022; The Journal of Immunology 200, no. 10 (2018): 3319-3331; previously cited) in view of Rezankova (In 21st international scientific conference AMSE applications of mathematics and statistics in economics, pp. 1-10. 2018; newly cited), as applied above to claim 1, and in further view of Wikipedia (Computation Offloading; publicly available 2016; previously cited on PTO892 mailed 12/18/2024).
This rejection is newly recited and is necessitated by claim amendment.
The limitations of claim 1 have been taught in the rejection above by Jimenez-Carretero and Rezankova.
Claims 11-12:
The broadest reasonable interpretation of transmitting compressed spectral data across a network includes transferring data within a computer system using internal buses. Jimenez-Carretero teaches that spectral data was dimensionality reduced in R using a Barnes-Hut t-Distributed Stochastic Neighbor Embedding, which were then used to perform clustering in Phenograph which is another R application (pg. 3321, para. 2-3).
However, neither Jimenez-Carretero nor Rezankova disclose transmitting compressed spectral data to an additional device via a network, wherein the additional device performs the clustering and transmits the clustering results back to another, separate device.
Wikipedia discloses computation offloading that “refers to the transfer of certain computing tasks to an external platform, such as a cluster, grid, or a cloud. Offloading may be necessary due to hardware limitations of a computer system handling a particular task on its own” (pg. 1, para. 1).
When Wikipedia and Jimenez-Carretero are taken together, they suggest transmitting compressed data to a cloud server via network to perform clustering, wherein the result of the clustering is then received by the computing device of Jimenez-Carretero to perform less computationally expensive tasks.
It would have been prima facie obvious to one of ordinary skill in the art to have modified the method of Jimenez-Carretero and Rezankova for analyzing high-dimensional flow cytometry data by offloading the clustering as taught by Wikipedia. The motivation for doing so is taught by Jimenez-Carretero who states that computational limitations such as time, memory, and resources occur when jointly processing large and multiple samples for comparative analysis (pg. 3320, col. 2, last para.). One of ordinary skill in the art would have had a reasonable expectation of success to perform computation offloading because cloud/grid computing is a standard method for computationally expensive tasks.
Response to Arguments under 35 USC 103
Applicant's arguments filed 12/03/2025 have been fully considered but they are not persuasive.
Applicant’s remarks regarding Strehl and Bruggner are noted but are not persuasive because the instant rejection no longer relies on these references (pg. 14, para. 2 – pg. 16, para. 2 of Applicant’s remarks).
Applicant’s remarks in sections B-E on pg. 16 – pg. 17 of Applicant’s remarks are noted but are not persuasive in view of the new grounds of rejection necessitated by claim amendment.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685