DETAILED ACTION
The following is a first office action upon examination of application number 19/471925. Claims 1-7 and 12-14 are pending in the application and have been examined on the merits discussed below.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/2/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a first extraction unit, first model generation unit, first prediction unit, second extraction unit, second model generation unit, and second prediction unit.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Paragraphs 21 and 22 of the Specification describe a server/computer in connection with the units.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-7 and 12-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 1) Claims 1-7 and 12 are directed to a system comprising a computer; thus the system comprises a device or set of devices, and therefore, is directed to a machine which is a statutory category of invention. Claims 13 and 14 are directed to a non-transitory computer-readable medium, which is a manufacture, and this a statutory category of invention.
(Step 2A) The claims recite an abstract idea instructing how to predict a level of psychological stress of workers, which is described by claim limitations reciting:
…extract, for each object person, a similar worker group from a plurality of workers on the basis of a degree of similarity of a working style/attendance-status to a working style/attendance-status of an object person during a first period;
…generate, for each object person, a first prediction model for predicting a level of psychological stress of the object person by … using first information, as learning data, the first information including at least working style/attendance-status information indicating the working style/attendance-status of each worker included in the similar worker group, measurement information including an amount of activity and sleeping hours of each worker, action record information indicating records of the time of an action performed by each worker for each action type, and psychological stress information indicating a level of psychological stress of each worker; and
…predict, for each object person, a level of psychological stress of the object person from the first period onwards by inputting input data including at least the working style/attendance-status information, the measurement information, and the action record information of the object person to the first prediction model.
The identified limitations in the claims describing predicting a level of psychological stress of workers (i.e., the abstract idea) fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, which covers fundamental economic practice and managing personal behavior. Dependent claims 2, 3, 5, 6, 7, and 12 recite limitations that further narrow the abstract idea; therefore, these claims are also found to recite an abstract idea.
This judicial exception is not integrated into a practical application because additional elements such as the first extraction unit, first model generation unit, and first prediction unit and computer readable storage device in claim 1; the second extraction unit, second model generation unit, and second prediction unit in claim 4; the non-transitory computer-readable recording medium-recording a control program, the control program causing the computer to function as the first extraction unit, the first model generation unit, and the first prediction unit in claim 13; and the non-transitory computer-readable recording medium-recording a control program, the control program causing the computer to function as the second extraction unit, the second model generation unit, and the second prediction unit in claim 14, do not add a meaningful limitation to the abstract idea since these elements are only broadly applied to the abstract ideas at a high level of generality; thus, none of recited hardware offers a meaningful limitation beyond generally linking the abstract idea to a particular technological environment, in this case, implementation via a processor/computer.
Additional elements such as machine learning do not yield an improvement in the functioning of the computer itself, nor do they yield improvements to a technical field or technology; further, these additional elements are recited at a high level of generality and only generally link the abstract idea to a technological environment. Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
(Step 2B) The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration of the abstract idea into a practical application, the hardware additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Additional elements such as machine learning do not yield an improvement and only generally link the abstract idea to a technological environment. In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-7 and 12-14 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by JP2022080198A (Kamura).
As per claim 4, Kamura teaches: a prediction system comprising: ([0017] As shown in FIG. 1, the healthcare system includes a learning data input unit 10 and a data storage unit 20 for accumulating learning data. In addition, a learning prediction processing unit 30 that generates a prediction machine learning model for predicting the health condition of the prediction target person)
a second extraction unit configured to extract, for each object person, a similar worker group from a plurality of workers on the basis of a degree of similarity of an attendance status to an attendance status of an object person during a first period; ([0002] … employees are predicted by data classified (grouped) by "attendance data"…; attendance shows the work style. [0030] Data of a plurality of companies and employees are stored in each storage unit 21 to 28. [0040] (D) The work style data generation unit 31 accesses the attendance data storage unit 23, reads out the daily attendance time data and the attendance time data for each individual, and generates the attendance / leaving history data which is the time-series data of the date and time attendance… The time range is 3 to 6 months prior to the date [0041] … The work style data generation unit 31 accesses the attendance data storage unit 23, the personnel data storage unit 24, and the salary data storage unit 25, and the number of working days, total working hours, and vacation days of each month for each individual. Data on the presence or absence of personnel announcements and average salary (monthly amount) is read out, and attendance history data, which is time-series data related to work conditions, is generated. [0045] In step S3, the work style data classification unit 32 performs cluster classification (grouping) of each item data of the above items (A) to (F) by machine learning such as K-means. [0071] In step S24, the prediction target person data generation unit 36 inputs the prediction target person data to the prediction unit 37. In step S25, the prediction unit 37 classifies each of the items (A) to (G) of the prediction target person).
a second model generation unit configured to generate, for each object person, a second prediction model for predicting a level of psychological stress of the object person by machine learning using second information, as learning data, ([0031] …teacher data generation unit 34 as means for generating a prediction machine learning model. It is provided with a learning unit 35. Further, as a means for predicting the future health state of the prediction target person… [0036] …The work style data of each employee includes data from the company's perspective consisting of items (A) to (C) and data from the individual perspective of the employee composed of items (D) to (F). , It is possible to generate a machine learning model for prediction using the data of individual employees and the data of the company to which the employee belongs, and to judge and predict items related to the health of the person to be predicted. [0052] …the teacher data generation unit 34 sets the objective variable for each classification data in the "comprehensive judgment" of this medical examination, the "work burden" of this stress check, and the "work style and purpose". , "Health status and satisfaction"… [0054] (1) First, the teacher data generation unit 34 determines the objective variable side (the "comprehensive determination" of this medical examination, and the "work burden", "work style and purpose", and "work method and purpose" of this stress check. For each value of "health status and satisfaction" and "work environment (department)"), the explanatory variables ("work style data" and the previous "health check judgment data" and the previous "stress check judgment data") Create teacher data with the same amount of data. [0063] In step S14, the learning unit 35 learns the teacher data of (1) to (3) generated in step S11 with a plurality of machine learning algorithms to create a plurality of predictive machine learning models).
the second information including at least attendance status information indicating an attendance status of each worker included in the similar worker group, [0022] The attendance history data input by the attendance data input unit 13 to the data storage unit 20 includes monthly and daily data of each employee belonging to the company. Monthly attendance history data is data such as working hours, break hours, overtime hours, midnight overtime hours, and holiday commuting. The daily attendance history data is data such as attendance time, leaving time, going out start time, going out end time, break start time, and break end time. [0041] … The work style data generation unit 31 accesses the attendance data storage unit 23, the personnel data storage unit 24, and the salary data storage unit 25, and the number of working days, total working hours, and vacation days of each month for each individual. Data on the presence or absence of personnel announcements and average salary (monthly amount) is read out, and attendance history data, which is time-series data related to work conditions, is generated [0089] (8) By including the attendance / leaving history data (item (D)) such as the attendance time data and the leaving time data of each employee for each individual in the working style data, not only the length of working hours but also the working hours zone Work style data can be classified)
measurement information including an amount of activity and sleeping hours of each worker, ([0025] The lifestyle history data input by the lifestyle data input unit 16 to the data storage unit 20 includes exercise habits such as the number of steps (daily average) of each employee belonging to the company,… sleeping time (daily average) [0042] (F) The work style data generation unit 31 accesses the lifestyle data storage unit 26 and the medical data storage unit 27, and exercise habits such as the number of steps for each year and each month, smoking habits, drinking habits, etc. Generates lifestyle history data, which is time-series data such as eating habits such as meal time, sleeping time)
action record information indicating records of the time of an action performed by each worker for each action type, and ([0026] The medical history data input by the medical data input unit 17 to the data storage unit 20 includes treatment history data and drug history data of each employee belonging to the company. The treatment history data is historical data such as the date of consultation, the name of the medical institution, the name of the doctor, the name of the disease, and the history of treatment)
psychological stress information indicating a level of psychological stress of each worker; and ([0021] The stress check determination data input by the stress check data input unit 12 to the data storage unit 20 is, for example, an evaluation of the response items and factor items of the occupational stress simple questionnaire (57 items) of each employee [0054] …the explanatory variables ("work style data" and the previous "health check judgment data" and the previous "stress check judgment data")).
a second prediction unit configured to predict a level of psychological stress of the object person from the first period onwards by inputting input data including at least attendance status information, measurement information, and action record information of the object person to the second prediction model ([0067] … predicting the future judgment regarding the health of the employees who are the prediction targets (for example, one year after the implementation date of this medical examination and stress check). [0069] … the work style data composed of items (A) to (F). To generate. The time range for acquiring work style data is as shown in FIG. The work style data here is the data of the company to which the forecast target person specified by the personal identification data and the company identification data belongs for the items (A) to (C), and (D) to (F) are. , It is the data of the person to be predicted specified by the personal identification data. [0071] In step S24, the prediction target person data generation unit 36 inputs the prediction target person data to the prediction unit 37. [0072] …The prediction result output unit 38 outputs the prediction result to the prediction result display unit 61. There are five types of predictions: "comprehensive judgment" for medical examinations, "work burden" for stress checks, "work style and purpose", "health status and satisfaction" [0074] FIG. 25 shows a display example of the prediction result on the prediction result display unit 61. In the prediction results, the future "comprehensive judgment of medical examination" of the person to be predicted, "work burden" of stress check, "work style and purpose", "health condition and satisfaction" [0075] By the way, the prediction unit 37 can also make a prediction by an operator such as a prediction target person using work style data and / or a machine learning model for prediction).
As per claim 5, Kamura teaches: wherein the attendance status information is information indicating at least one of the number of leaves taken, the number of annual paid leaves taken, average work start time, average work end time, and average working hours ([0022] The attendance history data input by the attendance data input unit 13 to the data storage unit 20 includes monthly and daily data of each employee belonging to the company. Monthly attendance history data is data such as working hours, break hours, overtime hours, midnight overtime hours, and holiday commuting. The daily attendance history data is data such as attendance time, leaving time, going out start time, going out end time, break start time, and break end time. [0038] (B) The work style data generation unit 31 accesses the attendance data storage unit 23, the personnel data storage unit 24, and the salary data storage unit 25 to obtain the average number of working days and average total working hours of each month for each company. The average number of vacation days, the frequency of personnel announcements [0041] … vacation days of each month for each individual. Data on the presence or absence of personnel).
As per claim 6, Kamura teaches: wherein the second model generation unit uses the second information, as the learning data, the second information including the attendance status information, ([0017] As shown in FIG. 1, the healthcare system includes a learning data input unit 10 and a data storage unit 20 for accumulating learning data. [0018] The learning data input unit 10 provides the data storage unit 20 with various data related to employees [0040] (D) The work style data generation unit 31 accesses the attendance data storage unit 23, reads out the daily attendance time data and the attendance time data for each individual, and generates the attendance / leaving history data which is the time-series data of the date and time attendance. .. The time range is 3 to 6 months prior to the date of the medical examination or stress check) the measurement information, ([0025] The lifestyle history data input by the lifestyle data input unit 16 to the data storage unit 20 includes exercise habits such as the number of steps (daily average) of each employee belonging to the company,… sleeping time (daily average) [0042] (F) The work style data generation unit 31 accesses the lifestyle data storage unit 26 and the medical data storage unit 27, and exercise habits such as the number of steps for each year and each month, smoking habits, drinking habits, etc. Generates lifestyle history data, which is time-series data such as eating habits such as meal time, sleeping time) the action record information, ([0026] The medical history data input by the medical data input unit 17 to the data storage unit 20 includes treatment history data and drug history data of each employee belonging to the company. The treatment history data is historical data such as the date of consultation, the name of the medical institution, the name of the doctor, the name of the disease, and the history of treatment) and the psychological stress information of each worker during the first period ([0021] The stress check determination data input by the stress check data input unit 12 to the data storage unit 20 is, for example, an evaluation of the response items and factor items of the occupational stress simple questionnaire (57 items) of each employee [0054] …the explanatory variables ("work style data" and the previous "health check judgment data" and the previous "stress check judgment data")).
As per claim 12, Kamura teaches: wherein the action record information includes information regarding how each worker spends time that is not working hours and during which a desired action is selectable ([0025] The lifestyle history data input by the lifestyle data input unit 16 to the data storage unit 20 includes exercise habits … smoking habits (number of daily habits), and so on. It is related to lifestyle such as drinking habits (average number of times per week), drinking habits (daily drinking amount), eating habits (number of meals) [0042] …Generates lifestyle history data, which is time-series data such as eating habits such as meal time).
As per claim 14, Kamura teaches: a non-transitory computer-readable recording medium-recording a control program for causing a computer to function as the prediction system according to claim 4, the control program causing the computer to function as the second extraction unit, the second model generation unit, and the second prediction unit ([0013] Furthermore, the present invention is a computer program that causes a computer to function).
As per claim 1, this claim recites limitations substantially similar to those addressed by the rejection of claim 4, above; therefore, the same rejection applies.
As per claim 3, this claim recites limitations substantially similar to those addressed by the rejection of claim 6, above; therefore, the same rejection applies.
As per claim 7, this claim recites limitations substantially similar to those addressed by the rejection of claim 12, above; therefore, the same rejection applies.
As per claim 13, this claim recites limitations substantially similar to those addressed by the rejection of claim 14, above; therefore, the same rejection applies.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP2022080198A (Kamura); in view of US 2017/0116552 (Deodhar).
As per claim 2, Kamura teaches: the plurality of working styles…, and the first extraction unit extracts the similar worker group on the basis of … information … ([0038] (B) The work style data generation unit 31 accesses the attendance data storage unit 23, the personnel data storage unit 24, and the salary data storage unit 25 to obtain the average number of working days and average total working hours [0041] (E) The work style data generation unit 31 accesses the attendance data storage unit 23, the personnel data storage unit 24, and the salary data storage unit 25, and the number of working days, total working hours [0045] … the work style data classification unit 32 performs cluster classification (grouping) of each item data of the above items (A) to (F) by machine learning such as K-means. [0085] … work style data can be classified and a machine learning model for prediction can be generated).
Although not explicitly taught by Kamura, Deodhar teaches: wherein the plurality of working styles include at least teleworking, and …. teleworking rate information indicating a proportion of teleworking days to a total number of working days ([0554] … maps each day as work from office, work from home, or work from other. [0561] … The user Work Pattern analyser 332 determines the Work Pattern of the user for one week (or any other time range of interest [0593] … percentage of ‘work from home’ days=(workdays marked as ‘work from home’)/(workdays for the week) [0800] … percentage of work from the home days that week=(Σ(workdays marked as work from the home over all next level sub-units))/(workdays of the sub-unit)).
It would have been obvious, before the effective filing date of the claimed invention, for one of ordinary skill in the art to have modified the teachings of Kamura with the aforementioned teachings of Deodhar with the motivation of using telework information to group workers. Further, one of ordinary skill in the art would have recognized that applying the teachings of Deodhar to the system of Kamura would have yielded predictable results and doing so would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for the categorization of workers based on a teleworking rate.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2020/0134243 – discloses the clustering of employees that exhibit similar patterns.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALAN TORRICO-LOPEZ whose telephone number is (571)272-3247. The examiner can normally be reached M-F 10AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Beth Boswell can be reached at (571)272-6737. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALAN TORRICO-LOPEZ/ Primary Examiner, Art Unit 3625
